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Journal articles on the topic 'Video sensor based detection'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Tzes, Anthony, and William R. McShane. "Development of Prototype Video-Based Sensor for Vehicle Detection from Stand-Still Images." Transportation Research Record: Journal of the Transportation Research Board 1570, no. 1 (1997): 202–10. http://dx.doi.org/10.3141/1570-23.

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The design, development, and testing of a prototype wide-area traffic detection system are described. The video-based sensor computes the approximate number of vehicles present within an a priori defined observation area from stand-still images. This sensor is mostly oriented toward the traffic detection in congested intersections, in which sensors using existing radar, acoustic, and video-based technology are faced with critical obstacles caused by the automobile stoppage. The prototype system has been tested and found to perform satisfactorily in field studies.

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Lin, Congtian, Jiangning Wang, and Liqiang Ji. "An AI-based Wild Animal Detection System and Its Application." Biodiversity Information Science and Standards 7 (September 11, 2023): e112456. https://doi.org/10.3897/biss.7.112456.

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Rapid accumulation of biodiversity data and development of deep learning methods bring the opportunities for detecting and identifying wild animals automatically, based on artificial intelligence. In this paper, we introduce an AI-based wild animal detection system. It is composed of acoustic and image sensors, network infrastructures, species recognition models, and data storage and visualization platform, which go through the technical chain learned from Internet of Things (IOT) and applied to biodiversity detection. The workflow of the system is as follows:Deploying sensors for different de

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Fadhil, Faris Rai, and Ari Purno Wahyu Wibowo. "SMOKE DETECTION ON CNN BASED VIDEO SURVEILLANCE SYSTEM." Jurnal Darma Agung 31, no. 1 (2023): 377. http://dx.doi.org/10.46930/ojsuda.v31i1.3010.

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Forest fires are a serious problem that can cause extensive forest land and plantation areas to be damaged, this damage not only disrupts the habitat but the ecosystems in the forest, several studies have made an experiment to prevent forest fires, one of which is by using the help of electronic sensors installed in forest areas, this sensor works chemically by detecting heat or a change in the composition of the atmosphere present in the air and room temperature, from these changes the data is sent to the central station and a fire will be predicted, this method has a weakness including the n

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Li, Sujuan, and Shichen Huang. "Remote medical video region tamper detection system based on Wireless Sensor Network." EAI Endorsed Transactions on Pervasive Health and Technology 8, no. 31 (2022): e3. http://dx.doi.org/10.4108/eetpht.v8i31.702.

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INTRODUCTION: A new telemedicine video tamper detection system based on wireless sensor network is proposed and designed in this paper. OBJECTIVES: This work is proposed to improve the performance of telemedicine video communication and accurately detect the tamper area in telemedicine video. METHODS: The sensor nodes in the sensing layer are responsible for collecting telemedicine video information and transmitting the information to the data layer. The data layer completes the storage of information and transmits it to the processing layer. The detection module of the processing layer detect

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Cai, Wen-Yu, Jia-Hao Guo, Mei-Yan Zhang, Zhi-Xiang Ruan, Xue-Chen Zheng, and Shuai-Shuai Lv. "GBDT-Based Fall Detection with Comprehensive Data from Posture Sensor and Human Skeleton Extraction." Journal of Healthcare Engineering 2020 (June 25, 2020): 1–15. http://dx.doi.org/10.1155/2020/8887340.

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Since fall is happening with increasing frequency, it has been a major public health problem in an aging society. There are considerable demands to distinguish fall down events of seniors with the characteristics of accurate detection and real-time alarm. However, some daily activities are erroneously signaled as falls and there are too many false alarms in actual application. In order to resolve this problem, this paper designs and implements a comprehensive fall detection framework on the basis of inertial posture sensors and surveillance cameras. In the proposed system framework, data sourc

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Ho, Chao Ching, and Dan Wen Kuo. "IEEE 1451-Based Sensor Interfacing and Data Fusion for Fire Smoke Detection." Key Engineering Materials 613 (May 2014): 219–27. http://dx.doi.org/10.4028/www.scientific.net/kem.613.219.

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The performance of a fire sensor has a significant effect on fire detection. Today’s fire alarm systems, such as smoke and heat sensors, however are generally limited to a close proximity to the fire; and cannot provide additional information about fire circumstances. Thus, it is essential to design a suite of low-cost networked sensors that provide the capability of performing distributed measurement and control in real time. In this work, a wireless sensor system was developed for fire detection. The purpose of this paper is to analyze the integration of traditional fire sensors into intelli

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Fu, Ting, Joshua Stipancic, Sohail Zangenehpour, Luis Miranda-Moreno, and Nicolas Saunier. "Automatic Traffic Data Collection under Varying Lighting and Temperature Conditions in Multimodal Environments: Thermal versus Visible Spectrum Video-Based Systems." Journal of Advanced Transportation 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/5142732.

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Vision-based monitoring systems using visible spectrum (regular) video cameras can complement or substitute conventional sensors and provide rich positional and classification data. Although new camera technologies, including thermal video sensors, may improve the performance of digital video-based sensors, their performance under various conditions has rarely been evaluated at multimodal facilities. The purpose of this research is to integrate existing computer vision methods for automated data collection and evaluate the detection, classification, and speed measurement performance of thermal

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Ding, Yiwei, Chaeyeon Han, Pavan Seshadri, et al. "Toward audio-based sensing for pedestrian detection." Journal of the Acoustical Society of America 155, no. 3_Supplement (2024): A282. http://dx.doi.org/10.1121/10.0027509.

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The detection and counting of pedestrians plays a central role for the design of smart cities. Although the use of cameras for this task has been shown to have high accuracy, they come at a high cost and are susceptible to challenges such as poor lighting, fog, and obstructed views. Our study investigates audio-based pedestrian detection, combining potentially low cost sensors with advanced machine learning based audio analysis algorithms. With an audio sensor installed along the walkway, machine learning algorithms can tell from the audio whether there is a pedestrian or not, or how far the p

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Xu, Qichang. "Using Sensor Network in Motion Detection Based on Deep Full Convolutional Network Model." Complexity 2021 (May 20, 2021): 1–11. http://dx.doi.org/10.1155/2021/3909522.

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Aiming at the shortcomings of traditional moving target detection methods in complex scenes such as low detection accuracy and high complexity, and not considering the overall structure information of the video frame image, this paper proposes a moving-target detection based on sensor network. First, a low-power motion detection wireless sensor network node is designed to obtain motion detection information in real time. Secondly, the background of the video scene is quickly extracted by the time domain averaging method, and the video sequence and the background image are channel-merged to con

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Li, Qian, Rangding Wang, and Dawen Xu. "A Video Splicing Forgery Detection and Localization Algorithm Based on Sensor Pattern Noise." Electronics 12, no. 6 (2023): 1362. http://dx.doi.org/10.3390/electronics12061362.

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Video splicing forgery is a common object-based intra-frame forgery operation. It refers to copying some regions, usually moving foreground objects, from one video to another. The splicing video usually contains two different modes of camera sensor pattern noise (SPN). Therefore, the SPN, which is called a camera fingerprint, can be used to detect video splicing operations. The paper proposes a video splicing detection and localization scheme based on SPN, which consists of detecting moving objects, estimating reference SPN, and calculating signed peak-to-correlation energy (SPCE). Firstly, fo

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Prakash, S. Suriya, Rapaka Usha, N. Karuppiah, S. Saravanan, M. Kalaiyarasi, and K. Karunanithi. "Smart Home and Security Systems: An IoT-Based Approach Utilizing ESP 32 and Multi-sensor Integration." E3S Web of Conferences 616 (2025): 02004. https://doi.org/10.1051/e3sconf/202561602004.

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This paper introduces an innovative IoT-based smart home and security system employing the ESP 32 microcontroller for comprehensive automation and safety. The system integrates various sensors, including a rain sensor for precipitation detection, a fire sensor for early fire warnings, a gas sensor for identifying flammable gases like LPG, and a moisture sensor to manage garden irrigation based on humidity levels. Additionally, a unique QR code calling bell system with video capability allows visitors to initiate a video call notification on the homeowner’s mobile device, facilitating remote do

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Liu, Cai Xia, and Fang Yi Xie. "A Perimeter Intrusion Detection System (PIDS) Based on Sensor Network." Applied Mechanics and Materials 568-570 (June 2014): 468–72. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.468.

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A perimeter intrusion detection system (PIDS) based on sensor network is proposed and designed for safeguarding important area from illegal intrusion. The PIDS consists of a front-end detection sub-system, a control center sub-system, anassociated sub-system, a network transmission sub-system and a power supply sub-system. The front-end detection sub-system is a sensor network containing a large number of smart sensors, which are of different types and functions. The control center sub-system is an integrated control platform based on the special analysis software, which can accurately identif

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Liu, Jun, Shenghua Gong, Wenxue Guan, Benyuan Li, Haobo Li, and Jiaxin Liu. "Tracking and Localization based on Multi-angle Vision for Underwater Target." Electronics 9, no. 11 (2020): 1871. http://dx.doi.org/10.3390/electronics9111871.

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With the cost reduction of underwater sensor network nodes and the increasing demand for underwater detection and monitoring, near-land areas, shallow water areas, lakes and rivers have gradually tended to densely arranged sensor nodes. In order to achieve real-time monitoring, most nodes now have visual sensors instead of acoustic sensors to collect and analyze optical images, mainly because cameras might be more advantageous when it comes to dense underwater sensor networks. In this article, image enhancement, saliency detection, calibration and refraction model calculation are performed on

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Fourie, Christiaan M., and Hermanus Carel Myburgh. "An Intra-Vehicular Wireless Multimedia Sensor Network for Smartphone-Based Low-Cost Advanced Driver-Assistance Systems." Sensors 22, no. 8 (2022): 3026. http://dx.doi.org/10.3390/s22083026.

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Advanced driver-assistance system(s) (ADAS) are more prevalent in high-end vehicles than in low-end vehicles. Wired solutions of vision sensors in ADAS already exist, but are costly and do not cater for low-end vehicles. General ADAS use wired harnessing for communication; this approach eliminates the need for cable harnessing and, therefore, the practicality of a novel wireless ADAS solution was tested. A low-cost alternative is proposed that extends a smartphone’s sensor perception, using a camera-based wireless sensor network. This paper presents the design of a low-cost ADAS alternative th

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Kusna, Mu'azaidin Nur, Zainal Abidin, and Ulul Ilmi. "The Design of Microcontroller-Based Detection Tools and Rat Pest Repellent on Rice Seeds." JEEMECS (Journal of Electrical Engineering, Mechatronic and Computer Science) 4, no. 2 (2021): 45–50. http://dx.doi.org/10.26905/jeemecs.v4i2.4401.

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The control of rat pest in rice nurseries to obtain increased production continues to be done. In an effort to overcome the problem of rat pest various alternative controls have been carried out, both in technical culture, physical mechanics, and chemically. To reduce the undesirable effects of using chemicals to control rat pets, it is necessary to use other alternative rat pets control. Based on these problems, this study conducted various test to create a design that could be used to repel rat pests. The method used includes electrical and systematic design. The steps taken include the sate

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Yoedistira, Chresiani Destianita, Muhammad Hilmi Afthoni, and Rokiy Alfanaar. "Silver nanoparticle based alcohol sensor manufacturing training for detection of halal drinks." Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang 6, no. 4 (2021): 613–19. http://dx.doi.org/10.26905/abdimas.v6i4.5124.

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Nowadays, halal drinks are one of the sectors that get the attention of various parties. Alcohol in beverages is important in determining the halalness of a beverage. Halal detection can be done using chemical sensors. In the Pharmacy Department, knowledge of analysis using sensors is rare. Therefore, this community service program was carried out to introduce qualitative methods of quick and simple alcohol analysis. The participants are students of the Pharmacy Department of STIKES Anwar Medika in Sidoarjo City. Participants are communities engaged in the health sector who have an interest in

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Li, Xiaofeng, Yao-Jan Wu, and Yi-Chang Chiu. "Volume Estimation using Traffic Signal Event-Based Data from Video-Based Sensors." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 6 (2019): 22–32. http://dx.doi.org/10.1177/0361198119842120.

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Traffic volume data is one of the most critical variables for signal retiming. However, collecting traffic volume manually can be time-consuming and costly. In recent years, video-based sensor systems have been applied on signalized intersections for signal timing control. The detectors in video-based sensors generate large amounts of real-time high-resolution event-based data, including signal status and detection status data. The vehicle arrivals for each detection event is a stochastic process and has a relationship with the signal status and the detection duration (time occupancy). Therefo

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B Priyanka. "Enhanced CNN and SVM with Adaptive Modality Switching and Audio-Based Video Summarization for Real-Time Agricultural Intrusion Detection." Journal of Information Systems Engineering and Management 10, no. 33s (2025): 880–96. https://doi.org/10.52783/jisem.v10i33s.5668.

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Smart intrusion detection in agriculture involves the use of IoT, AI, and sensor-based technologies to monitor fields for unauthorized human and animal activity. Advanced AI models enhance detection accuracy, reducing false alarms and improving response efficiency. The integration of edge computing and cloud-based analytics ensures rapid data processing, making intrusion detection systems more effective and reliable in modern agricultural security. Traditional security systems rely on either video-only or audio-only detection, and struggle in low-light conditions due to the absence of adaptive

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Zhang, Zhenwei, Shenming Zhang, Dong Ni, et al. "Multimodal Sensing for Depression Risk Detection: Integrating Audio, Video, and Text Data." Sensors 24, no. 12 (2024): 3714. http://dx.doi.org/10.3390/s24123714.

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Depression is a major psychological disorder with a growing impact worldwide. Traditional methods for detecting the risk of depression, predominantly reliant on psychiatric evaluations and self-assessment questionnaires, are often criticized for their inefficiency and lack of objectivity. Advancements in deep learning have paved the way for innovations in depression risk detection methods that fuse multimodal data. This paper introduces a novel framework, the Audio, Video, and Text Fusion-Three Branch Network (AVTF-TBN), designed to amalgamate auditory, visual, and textual cues for a comprehen

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Nizam, Yoosuf, Mohd Mohd, and M. Jamil. "Development of a User-Adaptable Human Fall Detection Based on Fall Risk Levels Using Depth Sensor." Sensors 18, no. 7 (2018): 2260. http://dx.doi.org/10.3390/s18072260.

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Unintentional falls are a major public health concern for many communities, especially with aging populations. There are various approaches used to classify human activities for fall detection. Related studies have employed wearable, non-invasive sensors, video cameras and depth sensor-based approaches to develop such monitoring systems. The proposed approach in this study uses a depth sensor and employs a unique procedure which identifies the fall risk levels to adapt the algorithm for different people with their physical strength to withstand falls. The inclusion of the fall risk level ident

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Kakara, Hiroyuki, Yoshifumi Nishida, Sang Min Yoon, Hiroshi Mizoguchi, and Tatsuhiro Yamanaka. "Development of Database of Children’s Fall Dynamics Using Daily Behavior Observing System." Journal of Robotics and Mechatronics 24, no. 5 (2012): 802–10. http://dx.doi.org/10.20965/jrm.2012.p0802.

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This paper describes the development of a fall database for biomechanical simulation. First, data on children’s daily activities were collected at a “sensor home,” which is a imitation daily living space. The sensor-based home comprises a video-surveillance system embedded into a daily-living environment and a wearable acceleration-gyro sensor. Falls were then detected from sensor data using a fall detection algorithm that we developed, and videos of detected falls were extracted from long-time recorded video. Extracted videos were used for fall motion analysis. A new Computer Vision (CV) algo

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SivaMani, Medam, Pinisetty Sushmanth, Matli Mokshagni, and Dr Sampath A. "Predictive analysis of pharmaceutical equipment." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–9. https://doi.org/10.55041/ijsrem40164.

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he pharmaceutical industry demands high standards of equipment reliability to ensure product quality and operational efficiency. This study explores a predictive maintenance framework that integrates machine learning and real-time video analysis to monitor equipment health and prevent failures. The system comprises three main functionalities: training a machine learning model to predict the Remaining Useful Life (RUL) of equipment based on historical sensor data, manual input for RUL prediction, and real-time video monitoring to detect equipment malfunctions. A RandomForestRegressor is employe

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Jin, Chengtuo, Tao Wang, Naji Alhusaini, et al. "Video Fire Detection Methods Based on Deep Learning: Datasets, Methods, and Future Directions." Fire 6, no. 8 (2023): 315. http://dx.doi.org/10.3390/fire6080315.

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Among various calamities, conflagrations stand out as one of the most-prevalent and -menacing adversities, posing significant perils to public safety and societal progress. Traditional fire-detection systems primarily rely on sensor-based detection techniques, which have inherent limitations in accurately and promptly detecting fires, especially in complex environments. In recent years, with the advancement of computer vision technology, video-oriented fire detection techniques, owing to their non-contact sensing, adaptability to diverse environments, and comprehensive information acquisition,

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M, Baritha Begum, Malaisamy K, Vishvamaharaja, Sethuramalingam, Kumar Tharun, and Shabarise Shri. "Real-Time Risk Detection in Industrial Settings Using IoT-Based Sensor Networks." International Journal of Multidisciplinary Research Transactions 6, no. 5 (2024): 90–101. https://doi.org/10.5281/zenodo.11180861.

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Industrial environments require robust systems to detect and mitigate safety risks in real-time. This paper presents an IoT-based risk detection system that uses a network of sensors—temperature, gas, fire, smoke, and flow—to monitor industrial sites. An Arduino NANO microcontroller processes the sensor data and triggers wireless alerts when anomalies are detected. Thing Speak integration allows for remote monitoring and data analysis. The system also employs video processing with YOLOv3 to ensure safety compliance, such as checking if workers are wearing helmets. Non-Maximum Suppr

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Zahradka, Nicole, Khushboo Verma, Ahad Behboodi, Barry Bodt, Henry Wright, and Samuel C. K. Lee. "An Evaluation of Three Kinematic Methods for Gait Event Detection Compared to the Kinetic-Based ‘Gold Standard’." Sensors 20, no. 18 (2020): 5272. http://dx.doi.org/10.3390/s20185272.

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Video- and sensor-based gait analysis systems are rapidly emerging for use in ‘real world’ scenarios outside of typical instrumented motion analysis laboratories. Unlike laboratory systems, such systems do not use kinetic data from force plates, rather, gait events such as initial contact (IC) and terminal contact (TC) are estimated from video and sensor signals. There are, however, detection errors inherent in kinematic gait event detection methods (GEDM) and comparative study between classic laboratory and video/sensor-based systems is warranted. For this study, three kinematic methods: coor

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Zhou, Yurui, and Guolong Zhao. "English Pronunciation Calibration Model Based on Multimodal Acoustic Sensor." Journal of Sensors 2022 (April 5, 2022): 1–10. http://dx.doi.org/10.1155/2022/2208653.

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In recent years, with the increasing frequency of international exchanges, people have gradually realized that language is a tool of communication and communication, and language learning should attach importance to oral teaching. However, in traditional classrooms, one of the problems faced by oral teaching is the mismatch of the teacher-student ratio: a teacher has to deal with dozens of students, one-on-one oral teaching and pronunciation guidance is impossible, and it is also affected by the teachers and the environment constraints. Therefore, the research on how to efficiently automate pr

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Wang, Zhanchao, Min Huang, Lulu Qian, Baowei Zhao, and Guangming Wang. "High-Altitude Balloon-Based Sensor System Design and Implementation." Sensors 20, no. 7 (2020): 2080. http://dx.doi.org/10.3390/s20072080.

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As a kind of large-scale unmanned aerial vehicle, a high-altitude balloon can carry a large load up to tens of kilometers in the near space for a long time, which brings a new way for the stratosphere atmospheric detection. In order to provide a suitable working environment for the near-space detection load, it is necessary to design a sensor system based on a high-altitude balloon, which is used to provide environmental temperature, height position, and attitude information, current working, and video surveillance. The high-altitude balloon-based sensor system designed in this paper had parti

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Farooq, Muhammad, Abul Doulah, Jason Parton, Megan McCrory, Janine Higgins, and Edward Sazonov. "Validation of Sensor-Based Food Intake Detection by Multicamera Video Observation in an Unconstrained Environment." Nutrients 11, no. 3 (2019): 609. http://dx.doi.org/10.3390/nu11030609.

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Video observations have been widely used for providing ground truth for wearable systems for monitoring food intake in controlled laboratory conditions; however, video observation requires participants be confined to a defined space. The purpose of this analysis was to test an alternative approach for establishing activity types and food intake bouts in a relatively unconstrained environment. The accuracy of a wearable system for assessing food intake was compared with that from video observation, and inter-rater reliability of annotation was also evaluated. Forty participants were enrolled. M

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Liu, Zhanwen, Shan Lin, Kunlun Li, and Anguo Dong. "Traffic Flow Video Detection System Based on Line Scan CMOS Sensor." Advanced Science Letters 7, no. 1 (2012): 478–83. http://dx.doi.org/10.1166/asl.2012.2733.

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Xiang, Xuezhi, Mingliang Zhai, Ning Lv, and Abdulmotaleb El Saddik. "Vehicle Counting Based on Vehicle Detection and Tracking from Aerial Videos." Sensors 18, no. 8 (2018): 2560. http://dx.doi.org/10.3390/s18082560.

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Vehicle counting from an unmanned aerial vehicle (UAV) is becoming a popular research topic in traffic monitoring. Camera mounted on UAV can be regarded as a visual sensor for collecting aerial videos. Compared with traditional sensors, the UAV can be flexibly deployed to the areas that need to be monitored and can provide a larger perspective. In this paper, a novel framework for vehicle counting based on aerial videos is proposed. In our framework, the moving-object detector can handle the following two situations: static background and moving background. For static background, a pixel-level

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Song, Shihua, Changyu Liu, Chenyu Liu, Tengfei Zhao, and Xinqiang Lan. "Design of a Live Pig Breeding System Based on ZigBee." Journal of Intelligence and Knowledge Engineering 1, no. 2 (2023): 23–27. http://dx.doi.org/10.62517/jike.202304204.

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This paper designed a breeding system based on ZigBee. The system consists of an environment detection module based on ZigBee technology and a video monitoring module based on BP neural network algorithm. The environment detection module is a wireless sensor network based on ZigBee technology. The sensors with different functions interact with the data through the ZigBee wireless communication module, and monitor the environment under the coordinated control of the router and the main control. The video surveillance module adopts the deep learning mode based on BP neural network algorithm, wit

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Deshmukh, S. G., and S. R. Gupta. "Quality Enhancement of Degraded Video and Object Tracking with Local Binary Pattern Approach." International Journal For Academic Research and Development 3, no. 1 (2021): 01–18. https://doi.org/10.5281/zenodo.6640622.

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Video surveillance has an objective to monitor a given environment and report the information about the observed activity that is of significant interest. In this respect, video usually utilizes electro-optical sensors that is video cameras to collect information from the environment. Moving object detection and tracking of a video image signals, by using visible light image sensor a thermal infrared, low light level imaging sensor uptake of the moving target. After the corresponding digital image processing, detection and extraction of moving targets in video file is performed [1]. The detect

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Ravi, G., Sd Afroz, K. Yamuna, and Sd Afsha. "CNN Based Wildlife Intrusion Detection and Alert System." International Transactions on Electrical Engineering and Computer Science 2, no. 1 (2023): 30–36. http://dx.doi.org/10.62760/iteecs.2.1.2023.40.

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Wildlife intrusion detection and alert systems are designed to detect and alert wildlife intrusion events, such as animals crossing highways, entering farms or protected areas, and approaching human settlements. This system often uses advanced technologies, such as cameras, sensors, and machine learning algorithms, to detect and identify animal species and behaviors. Convolutional neural networks (CNNs) are a type of deep learning algorithm commonly used for image and video analysis tasks, including object detection and classification. CNNs can learn to extract features from images and videos

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Liu, Cai Xia, and Kai Lu. "A Perimeter Intrusion Detection System Based on Sensor Network for Airport Application." Applied Mechanics and Materials 738-739 (March 2015): 50–55. http://dx.doi.org/10.4028/www.scientific.net/amm.738-739.50.

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Since the Security Infrastructure Construction Standard forCivil Aviation Transportation Airport (MH/T 7003-2008) revised by Civil Aviation Administration of China in 2008, more and more technologies have been applied in the airport security to meet the standard requirements. With the advent of internet of things era, the things interconnection and the coordination sensation of sensor network technology provide a new technical means for airport security. A perimeter intrusion detection system (PIDS) based on sensor network is proposed and designed for safeguarding airport flight area from ille

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Kim, Taeseong, Hyo-Rin Choi, and Young-Seon Jeong. "Autoencoder Based Fire Detection Model Using Multi-Sensor Data." Korean Institute of Smart Media 13, no. 4 (2024): 23–32. http://dx.doi.org/10.30693/smj.2024.13.4.23.

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Large-scale fires and their consequential damages are becoming increasingly common, but confidence in fire detection systems is waning. Recently, widely-used chemical fire detectors frequently generate lots of false alarms, while video-based deep learning fire detection is hampered by its time-consuming and expensive nature. To tackle these issues, this study proposes a fire detection model utilizing an autoencoder approach. The objective is to minimize false alarms while achieving swift and precise fire detection. The proposed model, employing an autoencoder methodology, can exclusively learn

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Morales Valdez, Jesús, and Leonardo Martínez Espíritu. "Smart Video Reproduction System Based on a Convolutional Neural Network (CCN)." Memorias del Congreso Nacional de Control Automático 7, no. 1 (2024): 566–71. https://doi.org/10.58571/cnca.amca.2024.096.

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Recent advances in computing and technological development have significantly enhanced the importance of algorithms such as neural networks, deep learning, and artificial intelligence in practical applications. Indeed, innovations like the Raspberry Pi and similar devices have facilitated neural network programming. This work details the development and implementation of a smart video reproduction system capable of recognizing various hand gestures, which are interpreted as instructions via a convolutional neural network (CNN). Using a presence sensor (PIR), the system determines when to activ

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Kumar, Aman, and Flavia D Gonsalves. "Computer Vision Based Fire Detection System Using OpenCV - A Case Study." Research & Review: Machine Learning and Cloud Computing 1, no. 2 (2022): 25–33. http://dx.doi.org/10.46610/rrmlcc.2022.v01i02.005.

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Conventional fire detection system was based mechanical sensor for fire detection. The smoke particles in the surrounding detected by sensors in the traditional fire detection system. However, this can also lead to false alarms. For example, a person smoking in a room of can activate a general fire alarm system. In addition, these systems are expensive and ineffective if the fire is far away from the detector. An alternatives fire detection system such as system based on computer vision and Image/video Processing technology to manage false alarms from conventional fire detection. One of the mo

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Pawar, Prof Pankaj S. "A 360 Degree IoT-Based Firefighting Robot System." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49523.

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Abstract - This project presents the A 360 Degree IoT-Based Firefighting Robot System, aimed at enhancing fire detection and suppression in hazardous environments. The robot is equipped with a rotating platform allowing full 360-degree surveillance and a suite of sensors, including flame, temperature, and gas sensors, to detect fire incidents in real-time. Upon detection, the robot activates a water or chemical extinguisher system and sends live updates to a remote monitoring system via IoT connectivity. The system also includes a camera module for live video streaming, enabling remote navigat

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Zhang, Zhongzi. "Analysis of Volleyball Video Intelligent Description Technology Based on Computer Memory Network and Attention Mechanism." Computational Intelligence and Neuroscience 2021 (December 28, 2021): 1–9. http://dx.doi.org/10.1155/2021/7976888.

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There are some problems in the process of video intelligent description and analysis of volleyball, such as poor effective information extraction rate and poor dynamic tracking effect. Based on this, combined with long-term and short-term memory network and attention mechanism, this paper designs an intelligent description model of volleyball video based on deep learning algorithm and studies how to improve the extraction rate of volleyball video information through intelligent detection hardware and image recognition technology. This paper first introduces the application of image recognition

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Zhang, Xinman, Jiayu Zhang, Mei Ma, et al. "A High Precision Quality Inspection System for Steel Bars Based on Machine Vision." Sensors 18, no. 8 (2018): 2732. http://dx.doi.org/10.3390/s18082732.

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Steel bars play an important role in modern construction projects and their quality enormously affects the safety of buildings. It is urgent to detect whether steel bars meet the specifications or not. However, the existing manual detection methods are costly, slow and offer poor precision. In order to solve these problems, a high precision quality inspection system for steel bars based on machine vision is developed. We propose two algorithms: the sub-pixel boundary location method (SPBLM) and fast stitch method (FSM). A total of five sensors, including a CMOS, a level sensor, a proximity swi

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Chen, Lun-Chi, Ruey-Kai Sheu, Wen-Yi Peng, Jyh-Horng Wu, and Chien-Hao Tseng. "Video-Based Parking Occupancy Detection for Smart Control System." Applied Sciences 10, no. 3 (2020): 1079. http://dx.doi.org/10.3390/app10031079.

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Street lighting is a fundamental aspect of security systems in homes, industrial facilities, and public places. To detect parking lot occupancy in outdoor environments, street light control plays a crucial role in smart surveillance applications that can perform robustly in extreme surveillance environments. However, traditional parking occupancy systems are mostly implemented for outdoor environments using costly sensor-based techniques. This study uses the Jetson TX2 to develop a method that can accurately identify street parking occupancy and control streetlights to assist occupancy detecti

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Li, Jinxiang, Huateng Liu, and Gonghao Nie. "Intelligent Monitoring System for the Elderly based on Posture Recognition." Frontiers in Computing and Intelligent Systems 7, no. 3 (2024): 61–66. http://dx.doi.org/10.54097/b8yfva80.

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In recent years, due to the gradual increase in the elderly population, elderly care has become an increasingly urgent issue, and the most common risk faced by the elderly is accidental falls. In this paper, an intelligent monitoring system for the elderly is studied, which includes a communication module, a sensor module and a state detection module. It can monitor the status of the elderly in real time and feed back to the server to activate the buzzer alarm function. In the communication module, WI-FI communication between esp32 microcontroller and PC is established to realize real-time tra

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Lehnert, Alexander, Falko Gawantka, Jonas During, Franz Just, and Marc Reichenbach. "XplAInable: Explainable AI Smoke Detection at the Edge." Big Data and Cognitive Computing 8, no. 5 (2024): 50. http://dx.doi.org/10.3390/bdcc8050050.

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Wild and forest fires pose a threat to forests and thereby, in extension, to wild life and humanity. Recent history shows an increase in devastating damages caused by fires. Traditional fire detection systems, such as video surveillance, fail in the early stages of a rural forest fire. Such systems would see the fire only when the damage is immense. Novel low-power smoke detection units based on gas sensors can detect smoke fumes in the early development stages of fires. The required proximity is only achieved using a distributed network of sensors interconnected via 5G. In the context of batt

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Liu, Long, and Yucui Pu. "Basketball player motion detection and motion mode analysis based on biomechanical sensors." Molecular & Cellular Biomechanics 21, no. 2 (2024): 354. http://dx.doi.org/10.62617/mcb.v21i2.354.

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Basketball player motion detection and analysis are crucial for optimizing performance and preventing injuries. Traditional methods often rely on visual observation and video analysis, lacking precision and real-time feedback. In this study, a unique novel Intelligent Bayesian tuned-augmented Support Vector Machine (IB-ASVM) was proposed for predicting basketball players’ motion modes and performance analysis using the biomechanical sensor data. Advancements in biomechanical sensors such as accelerometers, gyroscopes, and force sensors are deployed into ESP32 to build a player’s wearable gadge

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Carletti, Vincenzo, Antonio Greco, Alessia Saggese, and Bruno Vento. "A Smart Visual Sensor for Smoke Detection Based on Deep Neural Networks." Sensors 24, no. 14 (2024): 4519. http://dx.doi.org/10.3390/s24144519.

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The automatic detection of smoke by analyzing the video stream acquired by traditional surveillance cameras is becoming a more and more interesting problem for the scientific community thanks to the necessity to prevent fires at the very early stages. The adoption of a smart visual sensor, namely a computer vision algorithm running in real time, allows one to overcome the limitations of standard physical sensors. Nevertheless, this is a very challenging problem, due to the strong similarity of the smoke with other environmental elements like clouds, fog and dust. In addition to this challenge,

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Ye, Liang, Tong Liu, Tian Han, Hany Ferdinando, Tapio Seppänen, and Esko Alasaarela. "Campus Violence Detection Based on Artificial Intelligent Interpretation of Surveillance Video Sequences." Remote Sensing 13, no. 4 (2021): 628. http://dx.doi.org/10.3390/rs13040628.

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Campus violence is a common social phenomenon all over the world, and is the most harmful type of school bullying events. As artificial intelligence and remote sensing techniques develop, there are several possible methods to detect campus violence, e.g., movement sensor-based methods and video sequence-based methods. Sensors and surveillance cameras are used to detect campus violence. In this paper, the authors use image features and acoustic features for campus violence detection. Campus violence data are gathered by role-playing, and 4096-dimension feature vectors are extracted from every 1

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Yang, Jing Song, Xiu Ling He, and Li Xin Li. "Multi-Sensor Life Detection Synergy Platform Design." Applied Mechanics and Materials 442 (October 2013): 520–25. http://dx.doi.org/10.4028/www.scientific.net/amm.442.520.

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Life detection based on a single type of information sources detection technology cannot completely meet the needs of the earthquake relief. The existing life detection techniques are include of acoustic wave life detection, optical life detection and radar life detection. The advantages and existing problems of the three life detection techniques are analyzed. The advantages and present situation of multi-sensor detection synergy technique are given . We explained the platform structure , multi-sensor selection strategy, and information fusion model. Finally, the development direction of life

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Radha, D., M. Arun Kumar, Nagarjuna Telagam, and M. Sabarimuthu. "Smart Sensor Network-Based Autonomous Fire Extinguish Robot Using IoT." International Journal of Online and Biomedical Engineering (iJOE) 17, no. 01 (2021): 101. http://dx.doi.org/10.3991/ijoe.v17i01.19209.

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Fire explosion is among the main reasons for death in the world. The urban spaces have a lot of population, many systems have control over fire detection but not over control of fire due to lack of functionalities. The operation of the robot depends on the android application on the smartphone. It can also be communicated using Wireless fidelity technology. The motion detection technology is embedded in it, which can identify the objects or obstacles. With Arduino microcontroller and IoT technology, this robot can send emergency alerts in critical conditions, explore the compounds, and effecti

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Ortega-Zamorano, Francisco, Miguel A. Molina-Cabello, Ezequiel López-Rubio, and Esteban J. Palomo. "Smart motion detection sensor based on video processing using self-organizing maps." Expert Systems with Applications 64 (December 2016): 476–89. http://dx.doi.org/10.1016/j.eswa.2016.08.010.

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Yu, Ping, Bao Guo Dong, and Yu Juan Xue. "Electric Power Tower Inclination Angle Detection Method Based on SIFT Feature Matching." Applied Mechanics and Materials 236-237 (November 2012): 759–64. http://dx.doi.org/10.4028/www.scientific.net/amm.236-237.759.

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In video monitoring system of substation, in-process video inspection is used to detect abnormalities and provide corresponding solutions in a timely manner to avoid failures.As the common equipment，electric power tower’s inclination should be detected timely..It was hard to check the fault of tower inclination timely and accurately only by staff’s routine inspection,and it will spent much manpower and material resources by the manner of sensor. A manner of substation video inspection tower inclination angle detection based on SIFT feature matching and OTSU was presented in this paper. The tow

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