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Cheoi, Kyung Joo. "Temporal Saliency-Based Suspicious Behavior Pattern Detection." Applied Sciences 10, no. 3 (2020): 1020. http://dx.doi.org/10.3390/app10031020.
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The topic of suspicious behavior detection has been one of the most emergent research themes in computer vision, video analysis, and monitoring. Due to the huge number of CCTV (closed-circuit television) systems, it is not easy for people to manually identify CCTV for suspicious motion monitoring. This paper is concerned with an automatic suspicious behavior detection method using a CCTV video stream. Observers generally focus their attention on behaviors that vary in terms of magnitude or gradient of motion and behave differently in rules of motion with other objects. Based on these facts, the proposed method detected suspicious behavior with a temporal saliency map by combining the moving reactivity features of motion magnitude and gradient extracted by optical flow. It has been tested on various video clips that contain suspicious behavior. The experimental results show that the performance of the proposed method is good at detecting the six designated types of suspicious behavior examined: sudden running, colliding, falling, jumping, fighting, and slipping. The proposed method achieved an average accuracy of 93.89%, a precision of 96.21% and a recall of 94.90%.
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Wong, Khai Chiuan, and Mohd Ridzuan bin Ahmad. "Reviewing Approaches and Techniques for Detecting Suspicious Human Behavior: A Comprehensive Survey." ELEKTRIKA- Journal of Electrical Engineering 23, no. 2 (2024): 44–52. http://dx.doi.org/10.11113/elektrika.v23n2.538.
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The paper aims to review related works that focus on detecting suspicious human behavior using machine-learning techniques. Suspicious human behavior refers to behaviors that may indicate involvement in or preparation for a crime. Detecting such behaviors before a crime is committed allows law enforcement to take early action and prevent criminal activities. One of the challenges in developing an effective detection system for suspicious human behavior is the absence of a well-defined definition for such behaviors. Different definitions can lead to various methods for designing the detection system. The paper explores different definitions and their implications on the design of detection systems and mentions two types of methods that can be used for detecting suspicious human behavior: image-based methods and saliency mapping. Image-based methods utilize image or video recognition techniques to analyze objects held by individuals or recognize specific activities. Saliency mapping, on the other hand, focuses on emphasizing the movement of individuals using techniques like optical flow calculation to generate saliency maps. Additionally, the paper highlights the increasing popularity of embedded machine learning, particularly on portable platforms. The use of embedded machine learning allows for the deployment of machine learning models on mobile or lightweight devices. This can be relevant for developing efficient and portable systems for detecting suspicious human behavior. Overall, the paper aims to provide an overview of existing works in the field of suspicious human behavior detection using machine learning, exploring different definitions and methods employed in the literature.
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Cheng, Junyi, Xianfeng Zhang, Xiao Chen, Miao Ren, Jie Huang, and Peng Luo. "Early Detection of Suspicious Behaviors for Safe Residence from Movement Trajectory Data." ISPRS International Journal of Geo-Information 11, no. 9 (2022): 478. http://dx.doi.org/10.3390/ijgi11090478.
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Early detection of people’s suspicious behaviors can aid in the prevention of crimes and make the community safer. Existing methods that are focused on identifying abnormal behaviors from video surveillance that are based on computer vision, which are more suitable for detecting ongoing behaviors. While criminals intend to avoid abnormal behaviors under surveillance, their suspicious behaviors prior to crimes will be unconsciously reflected in the trajectories. Herein, we characterize several suspicious behaviors from unusual movement patterns, unusual behaviors, and unusual gatherings of people, and analyze their features that are hidden in the trajectory data. Meanwhile, the algorithms for suspicious behavior detection are proposed based on the main features of the corresponding behavior, which employ spatiotemporal clustering, semantic annotation, outlier detection, and other methods. A practical trajectory dataset (i.e., TucityLife) containing more than 1000 suspicious behaviors was collected, and experiments were conducted to verify the effectiveness of the proposed method. The results indicate that the proposed method for suspicious behavior detection has a recall of 93.5% and a precision of 87.6%, demonstrating its excellent performance in identifying the possible offenders and potential target places. The proposed methods are valuable for preventing city crime and supporting the appropriate allocation of police resources.
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Talekar, P. R. "Suspicious Activity Detection." International Journal of Advance and Applied Research 5, no. 17 (2024): 31–38. https://doi.org/10.5281/zenodo.12164825.
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In light of the recent spike in anti-social behavior, security has been given top attention. Many firms have installed CCTVs to keep an eye on people and their interactions all the time. In a modern country of 64 million people, each person is photographed thirty times a day. A vast quantity of video data is generated and stored for a predetermined period of time. A picture taken at 25 frames per second at a resolution of 704x576 will generate over 20 gigabytes of data per day. Because it requires a workforce and their full attention, it is almost impossible for humans to continually monitor data to decide whether events are unusual. This implies that automation of the same is required.
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Ghosh, Sumon, Prasham Shah, Aditya Ghadge, and Vaibhav Sanghavi. "Suspicious Activity Detection." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (2022): 113–16. http://dx.doi.org/10.22214/ijraset.2022.47186.
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Abstract: In today's insecure world, video surveillance systems play a significant role in keeping both indoors and outdoors secure. Real-time applications can utilize video surveillance components, such as behavior recognition, understanding and classifying activities as normal or suspicious. People are at risk from suspicious activities when it comes to the potential danger they pose. Detecting criminal activities in urban and suburban areas is necessary to minimize such incidents as criminal activity increases. The early days of surveillance were carried out manually by humans and involved a lot of fatigue, since suspicious activities were rare compared to everyday activities. Various surveillance approaches were introduced with the advent of intelligent surveillance systems. This paper analyzes two cases that could pose a threat to human lives if ignored, namely the detection of gun-related crimes, the detection of abandoned luggage, the detection of human violence, the detection of lock hammering, the theft of wallets, and the tempering of ATMs on surveillance video frames. In these papers they have used a neural network model that is Faster R-CNN and YOLOv3 technique to detect these activities.
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Zhu, Shao Ping, and Yu Hua Chen. "A Novel Approach Automatic Detection of Suspicious Behavior." Advanced Materials Research 962-965 (June 2014): 2838–41. http://dx.doi.org/10.4028/www.scientific.net/amr.962-965.2838.
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We propose an efficient method for automatic detection of suspicious behavior in video surveillance data. First of all, we cluster a set of sequences labeled as normal or suspicious. Then, we assign new observation sequences to behavior clusters. We label a sequence as suspicious if it maps to an existing model of suspicious behavior or does not map to any existing model according to the corresponding HMMs. We evaluate our proposed method on a real-world video surveillance and find that the method is very effective at detecting suspicious behavior.
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Gaware, Rutik. "Suspicious Activity Detection." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (2023): 1632–35. http://dx.doi.org/10.22214/ijraset.2023.57711.
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Abstract: Unpredictable activities is guessing a person's physical location or joint activity based on images or videos. The project will require the use of neural networks to detect human activities from CCTV images. Human behavior is one of the fundamental problems in computer vision and has been studied for over 15 years. This is important because the number of applications that can benefit from the search function is huge. For example, it is used in applications such as human prediction, video analysis, animal analysis and behavior understanding, language recognition, human relations-computer, and characters are less powerful. While low-cost depth sensors have limitations such as being limited to indoor use, their low-noise data and low depth make it difficult to estimate humans from depth images. Therefore, we plan to use neural networks to solve these problems. Analyzing human activity through image analysis is an active area of research in image processing and computer vision. Thanks to visual monitoring, human activities in public places such as bus stops, train stations, airports, banks, shopping malls, schools, parking lots and roads can be monitored in order to prevent attacks, theft, accidents, illegal parking, violence, fights and chain events. purse snatching etc. illegal and other activities. It is very difficult to monitor public places regularly, so there is a need for intelligent video surveillance that can monitor people's activities in real time and divide them into tasks, normal and abnormal activities; and can generate an alarm. Often research is done with images rather than video. Also, none of the published articles attempt to use CNNs to detect suspicious activity.
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Leong, Dexter Sing Fong, Hao Feng Chan, Shakir Hussain Naushad Mohamed, et al. "Suspicious Behavior Detection Using Computer Vision." Proceedings of International Conference on Artificial Life and Robotics 30 (February 13, 2025): 729–34. https://doi.org/10.5954/icarob.2025.os26-3.
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Saghehei, Ehsan, and Azizollah Memariani. "Suspicious Behavior Detection in Debit Card Transactions using Data Mining." Information Resources Management Journal 28, no. 3 (2015): 1–14. http://dx.doi.org/10.4018/irmj.2015070101.
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The approach used in this paper is an implementation of a data mining process against real-life transactions of debit cards with the aim of detecting suspicious behavior. The framework designed for this purpose has been obtained through merging supervised and unsupervised models. First, due to unlabeled data, Twostep and Self-Organizing Map algorithms have been used in clustering the transactions. A C5.0 classification algorithm has been applied to evaluate supervised models and also to detect suspicious behaviors. An innovative plan has been designed to evaluate hybrid models and select the most appropriate model for the solution of the fraud detection problem. The evaluation of the models and the final analysis of the data took place in four stages. The appropriate hybrid model was selected from among 16 models. The results show a high ability of selected model in detecting suspicious behavior in transactions involving debit cards.
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Poornima, S., and M. Geethanjali. "Shilling Attack Detection in User Based Recommendation System." Data Analytics and Artificial Intelligence 3, no. 2 (2023): 85–94. http://dx.doi.org/10.46632/daai/3/2/17.
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The majority of the existing unsupervised methods for detecting shilling attacks are based on user rating patterns, ignoring the differences in rating behavior between legitimate users and attack users. These methods have low accuracy in detecting different shilling attacks without having any prior knowledge of the attack types. We provide a novel unsupervised shilling assault detection technique based on an examination of user rating behavior in order to overcome these constraints. By first examining the deviation of rating tendencies on each item, we are able to determine the target item(s) and the accompanying goals of the attack users. Based on the results of this study, a group of suspicious users is then created. Second, we examine the users' rating behaviors in terms of their rating and interest preferences. Finally, using measurements of user rating behavior, we determine the suspicious degree and identify attack users within the collection of suspicious users. The Movie Lens 1M dataset, the sampled Amazon review dataset, and the Netflix dataset all show how good the suggested detection model.
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Ouivirach, Kan, Shashi Gharti, and Matthew N. Dailey. "Incremental behavior modeling and suspicious activity detection." Pattern Recognition 46, no. 3 (2013): 671–80. http://dx.doi.org/10.1016/j.patcog.2012.10.008.
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Ansari, Dr Vaqar, Aditya Ghadge, Prasham Shah, Sumon Ghosh, and Vaibhav Sanghavi. "Suspicious Activity Detection Using Different Models." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 2668–77. http://dx.doi.org/10.22214/ijraset.2023.50729.
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Abstract: In today's insecure world, video surveillance systems play a significant role in keeping both indoors and outdoors secure. Real-time applications can utilize video surveillance components, such as behavior recognition, understanding and classifying activities as normal or suspicious. People are at risk from suspicious activities when it comes to the potential danger they pose. Detecting criminal activities in urban and suburban areas is necessary to minimize such incidents as criminal activity increases. The early days of surveillance were carried out manually by humans and involved a lot of fatigue, since suspicious activities were rare compared to everyday activities. Various surveillance approaches were introduced with the advent of intelligent surveillance systems. This paper analyzes two cases that could pose a threat to human lives if ignored, namely the detection of gun-related crimes, the detection of abandoned luggage, the detection of human violence, the detection of lock hammering, the theft of wallets, and the tempering of ATMs on surveillance video frames. In these papers they have used a neural network model that is Faster R-CNN and YOLOv3 technique to detect these activities.
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Jiang, Meng, Peng Cui, and Christos Faloutsos. "Suspicious Behavior Detection: Current Trends and Future Directions." IEEE Intelligent Systems 31, no. 1 (2016): 31–39. http://dx.doi.org/10.1109/mis.2016.5.
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Singh, Sonakshi. "Active Chat Monitoring and Suspicious Chat Detection over Internet." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32634.
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Online communication platforms have become ubiquitous in modern society, enabling individuals and organizations to connect, collaborate, and communicate across geographical boundaries and diverse demographics. However, the proliferation of online chat environments also brings forth significant challenges related to security, privacy, and user safety. Unmonitored chat conversations pose various risks, including cyberbullying, predatory behavior, fraud, hate speech, and illegal activities. To address these risks, effective chat monitoring and suspicious chat detection mechanisms are essential. This paper provides a comprehensive overview of active chat monitoring and suspicious chat detection over the internet. It examines the evolution of online communication, highlighting key milestones and technological advancements that have shaped the landscape of digital interaction. The importance of chat monitoring and detection is underscored, emphasizing the need to mitigate risks associated with unmonitored chat conversations and safeguard users' safety, privacy, and well-being. Existing methods for chat monitoring and suspicious chat detection are explored, ranging from keyword-based filtering and sentiment analysis to machine learning models and user behavior analysis. These methods leverage advanced technologies such as artificial intelligence and natural language processing to analyze chat conversations in real-time, identifying patterns, anomalies, and indicators of suspicious or harmful behavior. The paper also discusses the challenges and limitations inherent in chat monitoring practices, including privacy concerns, false positives, scalability issues, and ethical considerations. Case studies and applications of chat monitoring in various contexts, such as law enforcement, social media moderation, corporate environments, and educational institutions, are examined to illustrate real-world implementations and outcomes. Ethical considerations related to privacy, transparency, fairness, and accountability in chat monitoring practices are addressed, emphasizing the importance of balancing security needs with respect for user rights and freedoms. Finally, future directions and emerging trends in active chat monitoring and suspicious chat detection are identified, highlighting opportunities for further research, development, and collaboration in this rapidly evolving field. By providing insights into the complexities and implications of monitoring online chat conversations, this paper aims to inform discussions, policies, and practices aimed at promoting safer, more responsible, and more inclusive online communication environments. Key Words-Online communication, Chat monitoring, Suspicious chat detection, Cybersecurity, Artificial intelligence, Natural language processing, Machine learning, Privacy, Ethics, Social media moderation, Cyberbullying, Predatory behaviour, Fraud detection, Hate speech detection, User behaviour analysis, Network traffic analysis, Privacy concerns, Ethical considerations, Real-time monitoring, Emerging technologies
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Sharma, Ritesh Manoj. "Active Chat Monitoring and Suspicious Chat Detection Over Internet." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32752.
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In the digital age, the proliferation of online communication platforms has facilitated unprecedented levels of connectivity and interaction. However, this interconnectedness has also introduced new challenges, particularly in ensuring the safety and security of users in online environments. One critical area of concern is the monitoring and detection of suspicious activities within chat platforms, where malicious actors may engage in harmful behaviors such as cyberbullying, harassment, or illicit activities. This research paper focuses on the development and implementation of active chat monitoring techniques for the detection of suspicious behavior over the internet. By leveraging advancements in natural language processing (NLP), machine learning, and data analytics, this study aims to explore effective methodologies for real-time monitoring and analysis of chat conversations to identify potentially harmful content. The paper will delve into various approaches for detecting suspicious patterns, including keyword analysis, sentiment analysis, and anomaly detection algorithms. Additionally, considerations for privacy, ethics, and legal implications surrounding chat monitoring will be discussed. Ultimately, this research endeavors to contribute to the enhancement of online safety measures by providing insights into the design and implementation of proactive monitoring systems capable of identifying and mitigating risks in virtual communication spaces. Keywords— Active Chat Monitoring, cybersecurity, user experience, digital era, security, user-controlled, effectiveness, evolving threat, Chat content analysis, Threat intelligence.
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Brewer, Neil, Audrey Bay Wei Ying, Robyn L. Young, and Yong-Hwee Nah. "Theory of mind and the detection of suspicious behavior." Journal of Applied Research in Memory and Cognition 7, no. 1 (2018): 123–31. http://dx.doi.org/10.1037/h0101817.
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Martinez, Duber, Humberto Loaiza, and Eduardo Caicedo. "Algorithm For Early Threat Detection By Suspicious Behavior Representation." IEEE Latin America Transactions 18, no. 05 (2020): 825–32. http://dx.doi.org/10.1109/tla.2020.9082909.
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Brewer, Neil, Audrey Bay Wei Ying, Robyn L. Young, and Yong-Hwee Nah. "Theory of Mind and the Detection of Suspicious Behavior." Journal of Applied Research in Memory and Cognition 7, no. 1 (2018): 123–31. http://dx.doi.org/10.1016/j.jarmac.2017.09.006.
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Shrushti, Thigale, Musale Jitendra, Shinde Swapnil, Deshmane Swamini, and Kale Harshad. "Deep Learning model for Anomaly Detection in Video Surveillance: A CNN Approach." Research and Applications: Embedded System 7, no. 2 (2024): 32–44. https://doi.org/10.5281/zenodo.11483938.
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<em>Suspicious activity encompasses a broad concept relating to actions, behaviors, or occurrences that give rise to concerns regarding potential illegality, threat, or ethical violations. This term is commonly employed in various domains such as law enforcement, cyber security, and financial sectors. Detecting and addressing suspicious activity often involves vigilant observation, data analysis, and the use of technology to identify patterns that deviate from established norms. Individual and community awareness is essential for recognizing and reporting such activities, contributing to the overall maintenance of safety and security. Effectively managing and responding to suspicious activity requires a combination of proactive measures, investigative tools, and collaborative efforts to prevent potential risks from escalating. With the increasing demand for robust security solutions, video surveillance systems play a crucial role in monitoring and safeguarding public spaces. This study focuses on enhancing the capabilities of video surveillance applications by employing Convolutional Neural Network (CNN) algorithms for the detection of suspicious activities. The proposed system leverages the power of deep learning to analyze video streams and identify anomalous behaviors indicative of potential threats or security breaches. The CNN algorithm is trained on a diverse dataset to learn and recognize patterns associated with normal activities as well as those considered suspicious. The model's ability to discern complex spatial and temporal relationships in video frames enables it to provide accurate and timely alerts. Key aspects of the CNN algorithm include feature extraction, spatial hierarchies, and temporal dependencies, enabling the system to discern subtle nuances in human behavior that may go unnoticed by traditional surveillance methods. The model is designed to adapt to dynamic environments and varying lighting conditions, ensuring robust performance in real-world scenarios. In the evaluation phase, the proposed system demonstrates promising results in terms of accuracy, precision, and recall, outperforming conventional video surveillance methods.</em>
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Dr., Mahesh Navle Aryan Jadhav Mahesh Shrikrishna Kadam Shalmali Dipak Karandikar Siddhi Anil Kate. "Suspicious Activity Detection in Exam Hall using Deep Learning." International Journal of Research in Engineering & Science 9, no. 2 (2025): 71–80. https://doi.org/10.5281/zenodo.15573101.
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<strong><em>Abstract</em></strong><strong><em>: </em></strong><em>Cheating in academic examinations compromises the integrity and fairness of educational assessments. Manual invigilation alone is often insufficient, especially in large-scale exams, where continuous monitoring is impractical. This study proposes a real-time, deep learning-based system for detecting suspicious activity in examination halls using surveillance footage. By leveraging Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, the system analyzes student behavior across video frames to flag anomalies such as unusual head movements, note passing, or collaboration. The solution enhances accuracy through temporal behavior analysis and operates non-intrusively using standard CCTV setups. Designed for scalability and minimal human oversight, the system aims to elevate examination security while maintaining student privacy.</em> <em> </em> <em> </em> <strong><em>Keywords</em></strong><strong><em>: </em></strong><em>Exam Surveillance, Deep Learning, CNN-LSTM, Suspicious Activity Detection, Computer Vision, Real- Time Monitoring, Academic Integrity.</em> <em> </em>
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Jadhav, Chaya, Rashmi Ramteke, and Rachna K. Somkunwar. "Smart Crowd Monitoring and Suspicious Behavior Detection Using Deep Learning." Revue d'Intelligence Artificielle 37, no. 4 (2023): 955–62. http://dx.doi.org/10.18280/ria.370416.
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Patil, Chetan, Saurbh Moynak, Yash Wagh, Bhavana Pathare, and Tanuja Mulla. "CogniWatch: Advanced Scene Analysis for Threat Detection and Public Safety." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem26323.
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Ensuring the safety of life and property through the deployment of high-quality CCTV cameras has become indispensable in our modern world. Manual monitoring of every moment is not feasible, and the unpredictable nature of human behavior makes distinguishing between suspicious and normal activities a formidable challenge. In this research, we introduce a novel approach utilizing Convolutional Neural Networks (CNN) to discern between suspicious and routine activities within an environment. Our proposed system is designed to automatically alert relevant authorities upon detecting potentially suspicious behavior. The effectiveness of any suspicious activity detection system hinges on several critical factors, including the quality of training data, the architecture of the Machine Learning model, and the operational environment. To maintain the system's accuracy and keep it adaptive to new and evolving threats, continuous monitoring, regular updates, and ongoing improvement are imperative. Our work underscores the importance of robust data sources and the careful design of CNN-based models to ensure the system's reliability in real-world applications. This research not only addresses the pressing need for automated surveillance but also emphasizes the significance of staying current and vigilant in the ever- changing landscape of safety and security. KeyWords: CNN, Object Detection, Anomaly, Threat, Hyperparameter Tunning.
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Kim, Sujeong, Chanwoong Hwang, and Taejin Lee. "Anomaly Based Unknown Intrusion Detection in Endpoint Environments." Electronics 9, no. 6 (2020): 1022. http://dx.doi.org/10.3390/electronics9061022.
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According to a study by Cybersecurity Ventures, cybercrime is expected to cost $6 trillion annually by 2021. Most cybersecurity threats access internal networks through infected endpoints. Recently, various endpoint environments such as smartphones, tablets, and Internet of things (IoT) devices have been configured, and security issues caused by malware targeting them are intensifying. Event logs-based detection technology for endpoint security is detected using rules or patterns. Therefore, known attacks can respond, but unknown attacks can be difficult to respond to immediately. To solve this problem, in this paper, local outlier factor (LOF) and Autoencoder detect suspicious behavior that deviates from normal behavior. It also detects threats and shows the corresponding threats when suspicious events corresponding to the rules created through the attack profile are constantly occurring. Experimental results detected eight new suspicious processes that were not previously detected, and four malicious processes and one suspicious process were judged using Hybrid Analysis and VirusTotal. Based on the experiment results, it is expected that the use of operational policies such as allowlists in the proposed model will significantly improve performance by minimizing false positives.
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Upadhya, Vibha, Pokale Akanksha Satish, Mulla Mushra Anjum Sameer, Vaishnavi Nalawade, and Dr Anupama Shankarrao Budhewar,. "Intrusion Detection in IOT." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40396.
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This study introduces a novel approach to enhance the efficiency and reliability of suspicious activity alert systems in network security. Our proposed solution utilizes an ensemble learning-based approach, which leverages multiple deep learning techniques. By combining the strengths of these models, we aim to enhance stratification correctness and minimize the fall-outs. Our system is specifically designed to identify suspicious activities by analyzing network traffic data, such as source IP address, destination IP address, port number, and protocol type. By detecting patterns that deviate from normal behavior, such as unusual connections or excessive data transfer, the system can trigger alerts that notify network administrators of potential security threats. The effectiveness of our proposed approach is demonstrated through rigorous experimental testing using real-world network traffic data. Additionally, our system can adapt and learn over time, making it more effective in detecting and preventing security breaches. In summary, our research contributes to the development of highly efficient and reliable suspicious activity alert systems in network security. Furthermore, our findings shed light on the potential benefits of ensemble learning techniques in this domain. Keywords—- Suspicious activity, Alert system, Network security, Deep learning, Intrusion detection
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Renushe, Prof Mrs A. H., Miss Varsha Poojary, Miss Salonee Shirsat, Miss Sakshi Sonawale, and Miss Pratiksha Yadav. "Suspicious Activity Tracking." International Journal for Research in Applied Science and Engineering Technology 12, no. 2 (2024): 1105–7. http://dx.doi.org/10.22214/ijraset.2024.58499.
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Abstract: Our offering includes a deep neural network model that can identify firearms in photographs and a machine learning and computer vision pipeline that can detect abandoned luggage in order to identify potential gun-based crime and circumstances involving abandoned luggage in surveillance film. Unusual behavior the technique of identifying undesired human activity in locations and circumstances is called detection. To do this, footage is converted into frames, and the processed frames are then used to analyze the people's sports. YOLOv3 is used to find a niche, in dubious sports like lock breaking and bag snatching, among others. Our gadget has a superb processing pace in addition to appropriate accuracy of detection. It is harder for computers to detect things in videos compared to images because of issues like blurriness or things getting blocked. They propose a solution called Shot Video Object Detector, which is a faster kind of detector for videos. It works by combining information from nearby frames to make better guesses about where objects are. Unlike other methods, Shot Video Object Detector does this by figuring out how things move between frames and then using that info to combine features. It also creates new features by borrowing information directly from neighboring frames using a special structure. Automated surveillance in public areas plays a crucial role in upholding law and order and proactively identifying potential risks to the public. Not only does the procedure automatically identify and detect known crooks, but it also tracks people's and things' movements and uses machine learning algorithms to alert the authorities to any questionable activity
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Kuzenbayev, B. A., B. T. Zhussupova, A. Zh Sarina, D. Zh Alippayeva, G. A. Babulova, and A. K. Karagozhina. "Principles of suspicious activity detection and programs for analyzing suspicious activity using video surveillance cameras." Bulletin of the National Engineering Academy of the Republic of Kazakhstan 93, no. 3 (2024): 121–31. http://dx.doi.org/10.47533/2024.1606-146x.55.
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This article delves into the principles of detecting suspicious activity, meticulously examining both logical and psychological facets of human behavior. It places a primary focus on diverse programs for analyzing suspicious activity using video surveillance cameras. A comprehensive overview is provided for key categories of suspicious activity, encompassing crowd clustering, rapid movement, intrusion into secure areas, and unattended items. Within the research framework, the advantages and drawbacks of leading programs for suspicious activity analysis through video surveillance are scrutinized. Furthermore, the article introduces the concept of an innovative program aimed at competing with existing solutions. This software aspires to amalgamate the strengths of competitors while avoiding their shortcomings. The research goal is to identify the potential for creating a highly effective software product capable of successfully competing and meeting the requirements of modern surveillance systems.
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Yadav, Pratik. "Predict, Identify and Alert on Suspicious Activity by Multiple Zone." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 4705–8. http://dx.doi.org/10.22214/ijraset.2023.52523.
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Abstract: Suspicious human activity detection in security capture is a study topic in image processing and vision. The mysterious identification of human activity from video surveillance is an area of study in both fields. Human activity can be monitored visually in conspicuous public spaces like bus depots, airports, railway stations, financial institutions, malls, schools, and universities to avoid terrorist activity, vandalism, accidents, prohibited parking spaces, vandalism, fighting chain theft, criminality, and other unusual behavior. Extremely difficult to continually monitor public spaces, thereby an innovative video surveillance installation system that can track people's movements in real-time, classify them as routine or odd, and send out an alert is needed. The field of visual surveillance to identify aberrant actions has seen a significant amount of publications in the last ten years. Furthermore There are a few surveys in the literature for recognising various abnormal activities, but none have reviewed various abnormal activities, but none of them have reviewed various abnormal activities. This study presents the stateof-the-art in the field of recognizing suspicious behavior from surveillance recordings during the past tenyears. We provide a brief outline of the risks and challenges associated with detecting suspicious human activity. This article examines six aberrant behaviors, including the identification of abandoned objects, theft, falls, traffic accidents, and unlawful parking, as wellas the detection of violence and fire. Generally speaking, we have covered all the processes that have been [1] Foreground object extraction, object identification based on tracking or non-tracking approaches, feature extraction, classification, activity analysis, and recognition are some ofthe techniques that have been used to identify human activity from surveillance movies in the literature. This paper's goal is to give field researchers a literature assessment of six different suspicious activity identificationsystems together with its broad framework.[1]
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Yan, Guanghua, Qiang Li, Dong Guo, and Xiangyu Meng. "Discovering Suspicious APT Behaviors by Analyzing DNS Activities." Sensors 20, no. 3 (2020): 731. http://dx.doi.org/10.3390/s20030731.
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As sensors become more prevalent in our lives, security issues have become a major concern. In the Advanced Persistent Threat (APT) attack, the sensor has also become an important role as a transmission medium. As a relatively weak link in the network transmission process, sensor networks often become the target of attackers. Due to the characteristics of low traffic, long attack time, diverse attack methods, and real-time evolution, existing detection methods have not been able to detect them comprehensively. Current research suggests that a suspicious domain name can be obtained by analyzing the domain name resolution (DNS) request to the target network in an APT attack. In past work based on DNS log analyses, most of the work would simply calculate the characteristics of the request message or the characteristics of the response message or the feature set of the request message plus the response message, and the relationship between the response message and the request message was not considered. This may leave out the detection of some APT attacks in which the DNS resolution process is incomplete. This paper proposes a new feature that represents the relationship between a DNS request and the response message, based on a deep learning method used to analyze the DNS request records. The algorithm performs threat assessment on the DNS behavior to be detected based on the calculated suspicious value. This paper uses the data of 4, 907, 147, 146 DNS request records (376, 605, 606 records after DNS Data Pre-processing) collected in a large campus network and uses simulation attack data to verify the validity and correctness of the system. The results of the experiments show that our method achieves an average accuracy of 97.6% in detecting suspicious DNS behavior, with the orange false positive (FP) at 2.3% and the recall at 96.8%. The proposed system can effectively detect the hidden and suspicious DNS behavior in APT.
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Navale, Dr. Mahesh, Aryan Arjun Jadhav, Mahesh Shrikrishna Kadam, Shalmali Dipak Karandikar, and Siddhi Anil Kate. "From Manual to Automated: A Computer Vision-Based Solution for Exam Cheating Detection." International Journal of Ingenious Research, Invention and Development (IJIRID) 3, no. 5 (2024): 414–19. https://doi.org/10.5281/zenodo.14066366.
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Cheating during exams is a widespread issue that undermines the credibility of educational assessments. Traditional invigilation methods, relying on manual supervision, often fall short in effectively detecting dishonest behavior, especially in large-scale exam settings. This study proposes an automated system that leverages computer vision and CCTV footage to detect suspicious behavior in real time, offering a scalable solution for maintaining exam integrity. Results demonstrate that the proposed method is both reliable and efficient, achieving high accuracy in detecting cheating behaviors within classroom environments. This automated approach represents a significant advancement in safeguarding the fairness and validity of exams.
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Yasmeen, Bushra, Haslina Arshad, and Hameedur Rahman. "SUSPICIOUS ACTIVITY DETECTION USING CCTV SURVEILLANCE VIDEO." Journal of Information System and Technology Management 6, no. 22 (2021): 60–70. http://dx.doi.org/10.35631/jistm.622006.
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Security has recently been given the highest priority with the rise in the number of antisocial activations taking place. To continuously track individuals and their interactions, CCTVs have been built in several ways. Every person is recorded on an image on average 30 times a day in a developed world with a community of 1.6 billion. The resolution of 710*570 captured at knitting will approximate 20 GB per day. Constant monitoring of human data makes it hard to judge whether the incident is an irregular one, and it is an almost uphill struggle when a population and its full support are needed. In this paper, we make a system for the detection of suspicious activity using CCTV surveillance video. There seems to be a need to demonstrate in which frame the behavior is located as well as which section of it allows the faster judgment of the suspicious activity is unusual. This is done by converting the video into frames and analyzing the persons and their activates from the processed frames. We have accepted wide support from Machine learning and Deep Learning Algorithms to make it possible. To automate that process, first, we need to build a training model using a large number of images (all possible images which describe features of suspicious activities) and a “Convolution Neural Network‟ using the Tensor Flow Python module. We can then upload any video into the application, and it will extract frames from the uploaded video and then that frame will be applied on a training model to predict its class such as suspicious or normal.
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Cai, Hao, Zhiguang Song, Jianlong Xu, Zhi Xiong, and Yuanquan Xie. "CUDM: A Combined UAV Detection Model Based on Video Abnormal Behavior." Sensors 22, no. 23 (2022): 9469. http://dx.doi.org/10.3390/s22239469.
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The widespread use of unmanned aerial vehicles (UAVs) has brought many benefits, particularly for military and civil applications. For example, UAVs can be used in communication, ecological surveys, agriculture, and logistics to improve efficiency and reduce the required workforce. However, the malicious use of UAVs can significantly endanger public safety and pose many challenges to society. Therefore, detecting malicious UAVs is an important and urgent issue that needs to be addressed. In this study, a combined UAV detection model (CUDM) based on analyzing video abnormal behavior is proposed. CUDM uses abnormal behavior detection models to improve the traditional object detection process. The work of CUDM can be divided into two stages. In the first stage, our model cuts the video into images and uses the abnormal behavior detection model to remove a large number of useless images, improving the efficiency and real-time detection of suspicious targets. In the second stage, CUDM works to identify whether the suspicious target is a UAV or not. Besides, CUDM relies only on ordinary equipment such as surveillance cameras, avoiding the use of expensive equipment such as radars. A self-made UAV dataset was constructed to verify the reliability of CUDM. The results show that CUDM not only maintains the same accuracy as state-of-the-art object detection models but also reduces the workload by 32%. Moreover, it can detect malicious UAVs in real-time.
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Digambar, Kauthkar, Pingle Snehal, Bansode Vijay, Idalkanthe Pooja, and Vani Sunita. "Suspicious Human Activity and Fight Detection using Deep Learning." International Journal of Innovative Science and Research Technology 7, no. 6 (2022): 390–92. https://doi.org/10.5281/zenodo.6791644.
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With the increasing number of shootings, knife attacks, terrorist attacks etc. in public places across the world, identifying the wrong behavior of human activities in public places has become an important task. This paper focuses on a deep learning approach to detect suspicious human activity and fight using convolutional neural networks from images and videos. We analyze different CNN architectures and compare their accuracy. We design our systems that can process video footage from cameras in real time and predict whether activity is suspicious or fight found or not. We also propose future developments that can be undertaken to detect and counter distrustful human activity in the public region.
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Pawar, Prof Dipali, Omkar Dhanwat, Sushant Shrivastav, Devendra Sutar, and Sourabh Yadav. "Suspicious Activity Detection from Video Surveillance Using CNN Algorithm." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 412–16. http://dx.doi.org/10.22214/ijraset.2023.51375.
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Abstract: Suspicious Activity is predicting the part or joint locations of someone from a picture or a video. Human suspicious Activity is one amongst the key issues in laptop vision that has been studied for over fifteen years. it's necessary due to the sheer variety of applications which mightlike Activity detection. for instance, human cause estimation is employed in applications as well as video police investigation, animal following and behavior understanding, language detection, advanced human-computer interaction, and marker less motion capturing. Low price depth sensors have limitations like restricted to indoor use, and their low resolution and yelling depth info build it troublesome to estimate human poses from depth pictures. Hence, we have a tendency to attempt to use neural networks to over- return these issues. Suspicious act recognition from police investigation video is an energetic analysis space of image process and laptop vision. Through the visual police investigation, human activities may be monitored in sensitive and public areas like busstations, railway stations, airports, banks, searching malls, faculty and faculties, parking tons, roads, etc. to stop act of terrorism, theft, accidents and ill-gotten parking, vandalism, fighting, chain snatching, crime and different suspicious activities.
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Ayed, M. B., S. Elkosantini, and M. Abid. "An Automated Surveillance System Based on Multi-Processor and GPU Architecture." Engineering, Technology & Applied Science Research 7, no. 6 (2017): 2319–23. https://doi.org/10.5281/zenodo.1119002.
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Video surveillance systems are a powerful tool applied in various systems. Traditional systems based on human vision are to be avoided due to human errors. An automated surveillance system based on suspicious behavior presents a great challenge to developers. Such detection is a rather complex procedure and also a rather time-consuming one. An abnormal behavior could be identified by: actions, faces, route, etc. The definition of the characteristics of an abnormal behavior still present a big problem. This paper proposes a specific architecture for a surveillance system. The aim is to accelerate the system and obtain a reliable and accelerated suspicious behavior recognition. Finally, the experiment section illustrates the results with comparison of some of the most recent approaches.
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G S, Chaya. "Unusual Crowd Activity Detection Using Open CV and Motion Influence Map." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 1232–36. http://dx.doi.org/10.22214/ijraset.2021.36550.
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Suspicious behavior is dangerous in public areas that may cause heavy causalities. There are various systems developed on the basis of video frame acquisition where motion or pedestrian detection occur but those systems are not intelligent enough to identify the unusual activities even at real time. It is required to recognized scamper situation at real time from video surveillance for quick and immediate management before any casualties. Proposed system focuses on recognizing suspicious activities and target to achieve a technique which is able to detect suspicious activity automatically using computer vision. Here system uses OpenCV library for classifying different kind of actions at real time.
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Dekkati, Sreekanth, Sai Srujan Gutlapalli, Upendar Rao Thaduri, and Venkata Koteswara Rao Ballamudi. "AI and Machine Learning for Remote Suspicious Action Detection and Recognition." ABC Journal of Advanced Research 11, no. 2 (2022): 97–102. http://dx.doi.org/10.18034/abcjar.v11i2.694.
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There is little question that the unchecked rise in population is to blame for the alarming increase in crime rates seen in industrialized and developing nations. As a direct consequence of this, there has been an increase in the number of calls for the use of video surveillance to address concerns about ordinary life and private property. As a consequence of this, we need a system that is capable of accurately recognizing human activity in real-time. Researchers have lately investigated machine learning and deep learning as potential methods for identifying human activities. To prevent fraud, we devised a technique that employs human activity recognition to examine a series of occurrences, evaluate whether or not a person is a suspect, and then take appropriate action. This system used deep learning to assign labels to the video based on human behavior. We can detect suspicious behavior based on the categories mentioned above of human activity and time duration by utilizing machine learning, which achieves an accuracy of around one hundred percent. This research article will detect suspicious behavior using optimal, effective, and quick methods. Using popular public data sets, the experimental findings described here highlight the approach's remarkable performance while only requiring a small amount of computational complexity.
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Nazir, Amril, Rohan Mitra, Hana Sulieman, and Firuz Kamalov. "Suspicious Behavior Detection with Temporal Feature Extraction and Time-Series Classification for Shoplifting Crime Prevention." Sensors 23, no. 13 (2023): 5811. http://dx.doi.org/10.3390/s23135811.
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The rise in crime rates in many parts of the world, coupled with advancements in computer vision, has increased the need for automated crime detection services. To address this issue, we propose a new approach for detecting suspicious behavior as a means of preventing shoplifting. Existing methods are based on the use of convolutional neural networks that rely on extracting spatial features from pixel values. In contrast, our proposed method employs object detection based on YOLOv5 with Deep Sort to track people through a video, using the resulting bounding box coordinates as temporal features. The extracted temporal features are then modeled as a time-series classification problem. The proposed method was tested on the popular UCF Crime dataset, and benchmarked against the current state-of-the-art robust temporal feature magnitude (RTFM) method, which relies on the Inflated 3D ConvNet (I3D) preprocessing method. Our results demonstrate an impressive 8.45-fold increase in detection inference speed compared to the state-of-the-art RTFM, along with an F1 score of 92%,outperforming RTFM by 3%. Furthermore, our method achieved these results without requiring expensive data augmentation or image feature extraction.
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Elhamod, Mohannad, and Martin D. Levine. "Automated Real-Time Detection of Potentially Suspicious Behavior in Public Transport Areas." IEEE Transactions on Intelligent Transportation Systems 14, no. 2 (2013): 688–99. http://dx.doi.org/10.1109/tits.2012.2228640.
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Mrs, K. Shireesha, Mounika Ramavath, Divya Gopularam, and Sreeja Gundeboina. "OBJECT ACTION DETECTION USING DEEP LEARNING." International Journal of Engineering Technology Research & Management (IJETRM) 09, no. 04 (2025): 364–68. https://doi.org/10.5281/zenodo.15275560.
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Object Action Detection is an intelligent video surveillance system developed to strengthen home security bydetecting abnormal human activities from recorded video footage. The system employs deep learning techniquesto analyzeActions and identify any suspicious behavior within the home environment.At the core of the system is a Spatial Autoencoder, which enables effective and accurate detection of unusualactivities in the video without high computational costs. Instead of relying on live streaming, the system processespre-recorded videos, detects abnormal actions, and captures key frames where such activity occurs.When a suspicious action is identified, the system automatically generates a mail alert to the homeowner, attachingthe relevant frame image for immediate review. These captured images are also stored for future reference,providing a reliable and intelligent way to monitor security events within the home from recorded footage.
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Shubham, R. Dhembare, and Sharad W. Mohod Dr. "Intrusion Detection System in Vehicular Ad-hoc Networks." International Journal of Innovative Science and Research Technology 7, no. 11 (2022): 1065–69. https://doi.org/10.5281/zenodo.7435133.
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Over the past few years, the field of intrusion detection in wireless networks has become more important. Insecure features in some wireless networks make the victims vulnerable to that attacks so any action can take time to implement. And another is that, as new techniques are evolving today, by making progress in the field of hacking, attackers will make every effort to infiltrate the system. Therefore, it is important to constantly monitor the system and detect suspicious behavior. So at such times, the intrusion detection system works to monitor the data, suspicious intrusions, and respond appropriately. In this perspective, this article presents a survey on previous studies of intrusion detection in wireless networks.
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Chen, Tong, Jiqiang Liu, Yalun Wu, et al. "Survey on Astroturfing Detection and Analysis from an Information Technology Perspective." Security and Communication Networks 2021 (December 1, 2021): 1–16. http://dx.doi.org/10.1155/2021/3294610.
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With the development of the Internet, user comments produced an unprecedented impact on information acquisition, goods purchase, and other aspects. For example, the user comments can quickly render a topic the focus of discussion in social networks. It can promote the sales of goods in e-commerce, and it influences the ratings of books, movies, or albums. Among these network applications and services, “astroturfing,” a kind of online suspicious behavior, can generate abnormal, damaging, and even illegal behaviors in cyberspace that mislead public perception and bring a bad effect on Internet users and society. Hence, the manner of detecting and combating astroturfing behavior has become highly urgent, attracting interest from researchers both from information technology and sociology. In the current paper, we restudy it mainly from the perspective of information technology, summarize the latest research findings of astroturfing detection, analyze the astroturfing feature, classify the machine learning-based detection methods and evaluation criteria, and introduce the main applications. Different from the previous surveys, we also discuss the new future directions of astroturfing detection, such as cross-domain astroturfing detection and user privacy protection.
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Adams, Peter, and Nancy M. Smith. "Understanding behavior detection technology: How it finds suspicious behaviors and meets requirements of new compliance environment." Journal of Investment Compliance 5, no. 1 (2004): 33–38. http://dx.doi.org/10.1108/15285810410636055.
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NICHEPORUK, A., A. NICHEPORUK, I. NEGA, Y. NICHEPORUK, and A. KAZANTSEV. "INFORMATION TECHNOLOGY FOR DETECTING METAMORPHIC VIRUSES BASED ON THE ANALYSIS OF THE BEHAVIOR OF APPLICATIONS IN THE CORPORATE NETWORK." Computer Systems and Information Technologies 1, no. 1 (2020): 60–67. http://dx.doi.org/10.31891/csit-2020-1-8.
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The problem of cybercrime is one of the greatest threats to the modern information world. Among a wide range of different types of malware, the leading place is occupied by viral programs that use mutations of their own software code, ie polymorphic and metamorphic viruses. The purpose of transforming your own code is for attackers to try to make their previous malware different (in terms of syntax, not in terms of semantics) with each new infection. According to a study conducted by Webroot in 2018, about 94% of all malware performs mutations in their software code. In addition, the problem of the prevalence of mutated software is complicated by the availability of free access to metamorphic generators, which allows you to import into malware metamorphic component. Therefore, the relevance of the development of new methods and information technologies focused on the detection of polymorphic and metamorphic software leaves no doubt. The paper proposed the information technology for detecting metamorphic viruses based on the analysis of the behavior of applications in the corporate network. The detection process is based on the analysis of API calls that describe the potentially dangerous behavior of the software application. After establishing the fact of suspicious behavior of the application, the disassembled code of the functional blocks of the suspicious application is compared with the code of the functional blocks of its modified version. Modified emulators are installed on network hosts to create a modified version of the software application. In order to increase the overall efficiency of detection of metamorphic viruses, information technology involves searching a match between the functional blocks of the metamorphic virus and its modified version. A fuzzy inference system is used to form a conclusion about the similarity of a suspicious program to a metamorphic virus. In case of insufficient manifestation of harmful behavior and in order to increase the level of reliability for the detection of metamorphic virus, other network hosts are involved.
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Somarriba, Oscar, Urko Zurutuza, Roberto Uribeetxeberria, Laurent Delosières, and Simin Nadjm-Tehrani. "Detection and Visualization of Android Malware Behavior." Journal of Electrical and Computer Engineering 2016 (2016): 1–17. http://dx.doi.org/10.1155/2016/8034967.
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Malware analysts still need to manually inspect malware samples that are considered suspicious by heuristic rules. They dissect software pieces and look for malware evidence in the code. The increasing number of malicious applications targeting Android devices raises the demand for analyzing them to find where the malcode is triggered when user interacts with them. In this paper a framework to monitor and visualize Android applications’ anomalous function calls is described. Our approach includes platform-independent application instrumentation, introducing hooks in order to trace restricted API functions used at runtime of the application. These function calls are collected at a central server where the application behavior filtering and a visualization take place. This can help Android malware analysts in visually inspecting what the application under study does, easily identifying such malicious functions.
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A Rao, Anagha, and Kanchana V. "Dynamic Approach for Detection of Suspicious Transactions in Money Laundering." International Journal of Engineering & Technology 7, no. 3.10 (2018): 10. http://dx.doi.org/10.14419/ijet.v7i3.10.15619.
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In the previous year, India has been among the most active nations in venturing up the battle against money laundering and related financial and security issues. The effort that likely got the most consideration was “demonetization” approach which intended to evacuate around 85% of the aggregate illegal cash available for use. To survey India's overall anti-money laundering (AML) system, it's more essential to center on the fundamental legitimate structure set up. In this paper, the proposed methodology is to analyze the user transactions and characterize based on their behavior of transactions. Then it focuses on the characterized transactions and obtains the connectivity among different accounts. To predict the suspicious transactions, we examine the log or trends found in previous years transactions of the user. By comparing the obtained data with the previous data, we will be able to predict suspicious transactions, providing the details are moved for further investigation.
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Patil, Shankargoud, and Kappargaon S. Prabhushetty. "Detection of Abnormal Activity to Alert the Nearby Persons via M-DNN Based Surveillance System." WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL 16 (December 20, 2021): 668–85. http://dx.doi.org/10.37394/23203.2021.16.61.
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In today's environment, video surveillance is critical. When artificial intelligence, machine learning, and deep learning were introduced into the system, the technology had progressed much too far. Different methods are in place using the above combinations to help distinguish various wary activities from the live tracking of footages. Human behavior is the most unpredictable, and determining whether it is suspicious or normal is quite tough. In a theoretical setting, a deep learning approach is utilized to detect suspicious or normal behavior and sends an alarm to the nearby people if suspicious activity is predicted. In this paper, data fusion technique is used for feature extraction which gives an accurate outcome. Moreover, the classes are classified by the well effective machine learning approach of modified deep neural network (M-DNN), that predicts the classes very well. The proposed method gains 95% accuracy, as well the advanced system is contrast with previous methods like artificial neural network (ANN), random forest (RF) and support vector machine (SVM). This approach is well fitted for dynamic and static conditions.
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Veena, Ch, I. Shweshitha, N. Nitheesha, and Harsha Priya Raulo. "The Data Mining Based Model for Detection of Fraudulent Behavior in Water Consumption." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (2022): 994–96. http://dx.doi.org/10.22214/ijraset.2022.46783.
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Abstract: There are many significant problems facing by water supplying companies and agencies because of fraudulent water consumption. Which is resulting a higher loss of income to water supplying agencies. Finding efficient measurements for detecting fraudulent activities has beenan active research area in recent years. To detect this fraudulent behaviour faced by water companies’ intelligent datamining techniques can be used to reduce the loss. This research explores the use of two classification techniques SVM and KNN to detect suspicious fraud water customers. The SVM based approach uses customer load profile attributes to expose abnormal behaviour that is known to be correlated with non-technical loss activities. The data has been collected from the historical data of the company billing system. The accuracy of the generated model obtained 74% which is better than the current manual prediction procedures.The system will help the company to predict suspicious water customers.
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do Nascimento, Vinicius D., Tiago A. O. Alves, Claudio M. de Farias, and Diego Leonel Cadette Dutra. "A Hybrid Framework for Maritime Surveillance: Detecting Illegal Activities through Vessel Behaviors and Expert Rules Fusion." Sensors 24, no. 17 (2024): 5623. http://dx.doi.org/10.3390/s24175623.
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Maritime traffic is essential for global trade but faces significant challenges, including navigation safety, environmental protection, and the prevention of illicit activities. This work presents a framework for detecting illegal activities carried out by vessels, combining navigation behavior detection models with rules based on expert knowledge. Using synthetic and real datasets based on the Automatic Identification System (AIS), we structured our framework into five levels based on the Joint Directors of Laboratories (JDL) model, efficiently integrating data from multiple sources. Activities are classified into four categories: illegal fishing, suspicious activity, anomalous activity, and normal activity. To address the issue of a lack of labels and integrate data-driven detection with expert knowledge, we employed a stack ensemble model along with active learning. The results showed that the framework was highly effective, achieving 99% accuracy in detecting illegal fishing and 92% in detecting suspicious activities. Furthermore, it drastically reduced the need for manual checks by specialists, transforming experts’ tacit knowledge into explicit knowledge through the models and allowing continuous updates of maritime domain rules. This work significantly contributes to maritime surveillance, offering a scalable and efficient solution for detecting illegal activities in the maritime domain.
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Ayed, M. B., S. Elkosantini, and M. Abid. "An Automated Surveillance System Based on Multi-Processor and GPU Architecture." Engineering, Technology & Applied Science Research 7, no. 6 (2017): 2319–23. http://dx.doi.org/10.48084/etasr.1645.
Full textAbstract:
Video surveillance systems are a powerful tool applied in various systems. Traditional systems based on human vision are to be avoided due to human errors. An automated surveillance system based on suspicious behavior presents a great challenge to developers. Such detection is a rather complex procedure and also a rather time-consuming one. An abnormal behavior could be identified by: actions, faces, route, etc. The definition of the characteristics of an abnormal behavior still present a big problem. This paper proposes a specific architecture for a surveillance system. The aim is to accelerate the system and obtain a reliable and accelerated suspicious behavior recognition. Finally, the experiment section illustrates the results with comparison of some of the most recent approaches.
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LIN, GUO-SHIANG, SIN-KUO CHAI, WEI-CHENG YEH, and YI-CHANG LIN. "SUSPICIOUS REGION DETECTION AND IDENTIFICATION BASED ON INTRA-/INTER-FRAME ANALYSES AND FUZZY CLASSIFIER FOR BREAST MAGNETIC RESONANCE IMAGING." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 03 (2014): 1450007. http://dx.doi.org/10.1142/s0218001414500074.
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Breast cancer is one of the leading causes of death from cancer in Taiwan. In this paper, we propose a feature-based scheme composed of preprocessing, feature extraction and a fuzzy classifier for suspicious region detection and identification. In the preprocessing stage, we first extract regions of interest and then coarsely determine suspicious regions via candidate screening. Some features are extracted based on intra-slice, texture and inter-slice analysis techniques for suspicious region identification. Intra-slice analysis evaluates the intensity and size of suspicious regions. To find a precise region, we propose a region growing algorithm based on ellipse-based approximation. In texture analysis, some texture cues are extracted from spatial and wavelet domains and integrated as a combined texture feature by using a neural network. Inter-slice analysis is based on the continuity characteristic and consistency of a suspicious region's size; the objective is to verify the static behavior of suspicious regions. Several magnetic resonance imaging (MRI) cases are utilized to evaluate the performance of the proposed scheme. Experimental results demonstrate that our scheme can not only extract regions of interest but also identify tumors well from magnetic resonance images.