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Relevant bibliographies by topics / Suspicious Activity Detection

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Academic literature on the topic 'Suspicious Activity Detection'

Author: Grafiati

Published: 2 June 2025

Last updated: 14 June 2025

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Journal articles on the topic "Suspicious Activity Detection"

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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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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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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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Bhambri, Pankaj, Sachin Bagga, Dhanuka Priya, Harnoor Singh, and Harleen Kaur Dhiman. "Suspicious Human Activity Detection System." December 2020 2, no. 4 (2020): 216–21. http://dx.doi.org/10.36548/jismac.2020.4.005.

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In collaboration with machine learning and artificial intelligence, anomaly detection systems are vastly used in behavioral analysis so that you can help in identity and prediction of prevalence of anomalies. It has applications in enterprise, from intrusion detection to system fitness tracking, and from fraud detection in credit score card transactions to fault detection in running environments. With the growing crime charges and human lack of confidence globally, majority of the countries are adopting precise anomaly detection systems to approach closer to a comfy area. Visualizing the Indian crime index which stands at 42. 38, the adoption of anomaly detection structures is an alarming want of time. Our own cannot be prevented with the aid of CCTV installations. These systems not simplest lead to identification on my own, but their optimized versions can help in prediction of unusual activities as properly.

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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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Kumbhar, Bhagyashri, Pranav Pisal, Kunal Kene, and Aditi Raut. "Suspicious Activity Detection Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 3745–48. http://dx.doi.org/10.22214/ijraset.2023.52486.

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Abstract: Various technologies have been utilized to implement the safety of life and property byinstalling high quality CCTV cameras. It is not possible to manually monitor each and every moment activity. Furthermore, in practical scenario the most unpredictable one is human behaviour and it is very difficult to find whether it is suspicious or normal. In this work the notion of CNN is used to detect suspicious or normal activity in an environment, and a systemis proposed that sends an alert message to the similarity authority, in case of predicting a suspicious activity. It's worth noting that the effectiveness of a suspicious activity detection system relies on the quality of the training data, the architecture of the Machine Learning model, and the deployment environment. Ongoing monitoring, regular updates, and continuousimprovement are important for maintaining the system's accuracy and adapting it to new and emerging types of suspicious activities

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Selvi, Esakky, Malaiyalathan Adimoolam, Govindharaju Karthi, et al. "Suspicious Actions Detection System Using Enhanced CNN and Surveillance Video." Electronics 11, no. 24 (2022): 4210. http://dx.doi.org/10.3390/electronics11244210.

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Suspicious pre- and post-activity detection in crowded places is essential as many suspicious activities may be carried out by culprits. Usually, there will be installations of surveillance cameras. These surveillance cameras capture videos or images later investigated by authorities and post-event such suspicious activity would be detected. This leads to high human intervention to detect suspicious activity. However, there are no systems available to protect valuable things from such suspicious incidents. Nowadays machine learning (ML)- and deep learning (DL)-based pre-incident warning alarm systems could be adapted to monitor suspicious activity. Suspicious activity prediction would be based on human gestures and unusual activity detection. Even though some methods based on ML or DL have been proposed, the need for a highly accurate, highly precise, low-false-positive and low-false-negative prediction system can be enhanced by hybrid or enhanced ML- or DL-based systems. This proposed research work has introduced an enhanced convolutional neural network (ECNN)-based suspicious activity detection system. The experiment was carried out and the results were claimed. The results are analyzed with the Statistical Package for the Social Sciences (SPSS) tool. The results showed that the mean accuracy, mean precision, mean false-positive rate, and mean false-negative rate of suspicious activity detections were 97.050%, 96.743%, 2.957%, and 2.927% respectively. This result was also compared with the convolutional neural network (CNN) algorithm. This research work can be applied to enhance the pre-suspicious activity alert security system to avoid risky situations.

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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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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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Dissertations / Theses on the topic "Suspicious Activity Detection"

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Kalutarage, H. K. "Effective monitoring of slow suspicious activites on computer networks." Thesis, Coventry University, 2013. http://curve.coventry.ac.uk/open/items/afdbba5c-2c93-41a7-90c3-2f0f3261b794/1.

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Slow and suspicious activities on modern computer networks are increasingly hard to detect. An attacker may take days, weeks or months to complete an attack life cycle. A particular challenge is to monitor for stealthy attempts deliberately designed to stay beneath detection thresholds. This doctoral research presents a theoretical framework for effective monitoring of such activities. The main contribution of this work is a scalable monitoring scheme proposed in a Bayesian framework, which allows for detection of multiple attackers by setting a threshold using the Grubbs’ test. Second contribution is a tracing algorithm for such attacks. Network paths from a victim to its immediate visible hops are mapped and profiled in a Bayesian framework and the highest scored path is prioritised for monitoring. Third contribution explores an approach to minimise data collection by employing traffic sampling. The traffic is sampled using the stratification sampling technique with optimum allocation method. Using a 10% sampling rate was sufficient to detect simulated attackers, and some network parameters affected on sampling error. Final contribution is a target-centric monitoring scheme to detect nodes under attack. Target-centric approach is quicker to detect stealthy attacks and has potential to detect collusion as it completely independent from source information. Experiments are carried out in a simulated environment using the network simulator NS3. Anomalous traffic is generated along with normal traffic within and between networks using a Poisson arrival model. Our work addresses a key problem of network security monitoring: a scalable monitoring scheme for slow and suspicious activities. State size, in terms of a node score, is a small number of nodes in the network and hence storage is feasible for very large networks.

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Books on the topic "Suspicious Activity Detection"

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Suspicious Activites. Harlequin Enterprises, Limited, 2016.

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Suspicious Activites. Harlequin Enterprises, Limited, 2016.

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Book chapters on the topic "Suspicious Activity Detection"

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Sarang, Shefali, Harshal Shinde, Vaishnavi Raut, Shubham Sonje, and Gargi Phadke. "Real-Time Suspicious Activity Detection." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2475-2_43.

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Pawade, Ashutosh, Rohan Anjaria, and V. R. Satpute. "Suspicious Activity Detection for Security Cameras." In Algorithms for Intelligent Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4862-2_22.

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Parris, Matthew Marlon Gideon, Hisham Al Assam, and Mohammad Athar Ali. "Suspicious Activity Detection for Defence Applications." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-72059-8_12.

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Saini, Anjali, Mushtaq Ahmed, and Kartikey Sharma. "Detection of Suspicious Activity in ATM Booth." In Intelligent Computing Techniques for Smart Energy Systems. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0214-9_97.

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Senthilkumar, T., and G. Narmatha. "Suspicious Human Activity Detection in Classroom Examination." In Advances in Intelligent Systems and Computing. Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-10-0251-9_11.

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Gorave, Asmita, Srinibas Misra, Omkar Padir, Anirudha Patil, and Kshitij Ladole. "Suspicious Activity Detection Using Live Video Analysis." In Proceeding of International Conference on Computational Science and Applications. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0790-8_21.

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Raut, Aditi, Santosh Indulkar, Kaushik Panchal, Prajwal Upadhyay, and Sony Kurian. "Automated Suspicious Activity Detection from Surveillance Videos." In Advances in Intelligent Systems and Computing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3608-3_5.

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Thombare, Puja, Vishal Gond, and V. R. Satpute. "Artificial Intelligence for Low Level Suspicious Activity Detection." In Algorithms for Intelligent Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4862-2_23.

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Liu, Xuan, Pengzhu Zhang, and Dajun Zeng. "Sequence Matching for Suspicious Activity Detection in Anti-Money Laundering." In Intelligence and Security Informatics. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-69304-8_6.

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Gulhane, V. S., and A. M. Bhugul. "Darknet for Gun and Suspicious Activity Detection and Crime Prediction." In Deep Learning Applications in Operations Research. Auerbach Publications, 2024. http://dx.doi.org/10.1201/9781032725444-8.

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Conference papers on the topic "Suspicious Activity Detection"

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Manuel, Abejun Ranz A., Riley Sebastianne D. Bughaw, and Charmaine C. Paglinawan. "Application of Pose Recognition for Suspicious-Activity Detection Alarm System." In 2024 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET). IEEE, 2024. http://dx.doi.org/10.1109/iicaiet62352.2024.10730643.

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Radhika, R., and A. Muthukumaravel. "Behavioral Analysis in Video Streams: Towards Automated Suspicious Activity Detection." In 2024 Asian Conference on Intelligent Technologies (ACOIT). IEEE, 2024. https://doi.org/10.1109/acoit62457.2024.10940053.

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Rajpoot, Lucky, and Rosy Madaan. "Comprehensive Review for Video Surveillance Based Suspicious Human Activity Detection." In 2024 2nd International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT). IEEE, 2024. https://doi.org/10.1109/icaiccit64383.2024.10912320.

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Kumar, Prabhakar, Sahil Singh, Md Tahseen Raza, and Yojna Arora. "Comparative Analysis of Suspicious Activity Detection Techniques in Surveillance Videos." In 2025 3rd International Conference on Disruptive Technologies (ICDT). IEEE, 2025. https://doi.org/10.1109/icdt63985.2025.10986382.

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Radhika, R., and A. Muthukumaravel. "Video Surveillance and Deep Learning Enhancing Security through Suspicious Activity Detection." In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). IEEE, 2024. http://dx.doi.org/10.1109/iacis61494.2024.10721938.

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R, Pavithra, Paul Steve Mithun B, Sofen T R, Shanmuga Prasath C, and Saravana V. "Real-Time Suspicious Activity Detection in Bank and ATM Environments Using Meta-Learning and Edge Computing." In 2025 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI). IEEE, 2025. https://doi.org/10.1109/icdsaai65575.2025.11011591.

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A., Alavudeen Basha, Parthasarathy P., and Vivekanandan S. "Expression of Concern for: Detection of Suspicious Human Activity based on CNN-DBNN Algorithm for Video Surveillance Applications." In 2019 Innovations in Power and Advanced Computing Technologies (i-PACT). IEEE, 2019. http://dx.doi.org/10.1109/i-pact44901.2019.10702766.

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V, Benedict Vinusha, V. Indhuja, Medarametla Varshitha Reddy, Nagalla Nikhitha, and Priyanka Pramila. "Suspicious Activity Detection using LRCN." In 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT). IEEE, 2023. http://dx.doi.org/10.1109/icssit55814.2023.10061045.

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Loganathan, Sathyajit, Gayashan Kariyawasam, and Prasanna Sumathipala. "Suspicious Activity Detection in Surveillance Footage." In 2019 International Conference on Electrical and Computing Technologies and Applications (ICECTA). IEEE, 2019. http://dx.doi.org/10.1109/icecta48151.2019.8959600.

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Barsagade, Kshitij, Sumeet Tabhane, Vishal Satpute, and Vipin Kamble. "Suspicious Activity Detection Using Deep Learning Approach." In 2023 1st International Conference on Innovations in High Speed Communication and Signal Processing (IHCSP). IEEE, 2023. http://dx.doi.org/10.1109/ihcsp56702.2023.10127155.

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Reports on the topic "Suspicious Activity Detection"

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Pennington, Adam G., John D. Strunk, John L. Griffin, Craig A. Soules, Garth R. Goodson, and Gregory R. Ganger. Storage-based Intrusion Detection: Watching storage activity for suspicious behavior. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada461142.

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Soramäki, Kimmo, and Florian Loecker. Preserving Data Sovereignty in National Fraud Portals- a Distributed Data Architecture. FNA, 2024. https://doi.org/10.69701/dhmk3850.

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As a result of the widespread increase in consumer fraud and scams, several countries are looking to establish or strengthen cross-bank, cross-platform, and cross-industry utilities to counter fraud and scams on the national level and to augment traditional efforts at individual financial institutions1. However, questions quickly arise about how data on consumers and their payments can and should be shared across institutions. Some data sharing across Financial Institutions (FIs) is crucial for fighting consumer fraud and scams as it enables organizations to track fraudulent funds across the payment system, detect suspicious activity and anomalies effectively, and flag suspicious transactions that may go unnoticed if a bank can only access its own transactions data. This collaborative approach enhances the overall security of the financial ecosystem, allows for quicker responses to emerging fraud tactics, and helps create comprehensive fraud detection systems. Consequently, it protects consumers by reducing the incidence and impact of fraud, thus fostering greater trust and confidence in banking and payment systems.

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