Journal articles: 'Surveillance video analysis'

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Pal, Ratnabali, Arif Ahmed Sekh, Debi Prosad Dogra, Samarjit Kar, Partha Pratim Roy, and Dilip K. Prasad. "Topic-based Video Analysis." ACM Computing Surveys 54, no. 6 (July 2021): 1–34. http://dx.doi.org/10.1145/3459089.

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Manual processing of a large volume of video data captured through closed-circuit television is challenging due to various reasons. First, manual analysis is highly time-consuming. Moreover, as surveillance videos are recorded in dynamic conditions such as in the presence of camera motion, varying illumination, or occlusion, conventional supervised learning may not work always. Thus, computer vision-based automatic surveillance scene analysis is carried out in unsupervised ways. Topic modelling is one of the emerging fields used in unsupervised information processing. Topic modelling is used in text analysis, computer vision applications, and other areas involving spatio-temporal data. In this article, we discuss the scope, variations, and applications of topic modelling, particularly focusing on surveillance video analysis. We have provided a methodological survey on existing topic models, their features, underlying representations, characterization, and applications in visual surveillance’s perspective. Important research papers related to topic modelling in visual surveillance have been summarized and critically analyzed in this article.

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Kardas, Karani, and Nihan Kesim Cicekli. "SVAS: Surveillance Video Analysis System." Expert Systems with Applications 89 (December 2017): 343–61. http://dx.doi.org/10.1016/j.eswa.2017.07.051.

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Nguyen, Cuong, Wu-chi Feng, and Feng Liu. "Hotspot: Making computer vision more effective for human video surveillance." Information Visualization 15, no. 4 (July 25, 2016): 273–85. http://dx.doi.org/10.1177/1473871616630015.

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Studies have shown that the human capability of monitoring multiple surveillance videos is limited. Computer vision techniques have been developed to detect abnormal events to support human video surveillance; however, their results are often unreliable, thus distracting surveillance operators and making them miss important events. This article presents Hotspot as a surveillance video visualization system that can effectively leverage noisy computer vision techniques to support human video surveillance. Hotspot consists of two views: a designated focus view to summarize videos with detected events and a video-bank view surrounding the focus view to display source surveillance videos. The focus view allows an operator to quickly dismiss false alarms and focus on true alarms. The video-bank view allows for extended human video analysis after an important event is detected. Hotspot further provides visual links to assist quick attention switch from the focus view to the video-bank view. Our experiments show that Hotspot can effectively integrate noisy, automatic computer vision detection results and better support human video surveillance tasks than the baseline video surveillance with no or only basic computer vision support.

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Talvitie-Lamberg, Karoliina. "Video Streaming and Internalized Surveillance." Surveillance & Society 16, no. 2 (July 14, 2018): 238–57. http://dx.doi.org/10.24908/ss.v16i2.6407.

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This paper aims to develop knowledge about the complicated ways in which the modern individual uses surveillance (techniques) and the ways surveillance uses the individual. My observational analysis of a videostreaming community reveals the central role that surveillance plays in participating and becoming visible in an online environment. The results show that through disciplinary and lateral surveillance, participants produced context-defined I-narrations and formed themselves following the normative judgment of the environment. The same mechanism may be observed in other videostreaming social media environments and the modern social media-saturated society in general. This is an inconspicuous way to produce surveillant individualism. Contrary to the notion of exploitative participation, this study reveals the productive power of surveillance. My research suggests that disciplinary power is integrated into the everyday in online DIY environments and it creates the space and framework for communication in these environments. Surveillance practices offer empowering means for forming identities.

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Pan, Tung-Ming, Kuo-Chin Fan, and Yuan-Kai Wang. "Object-Based Approach for Adaptive Source Coding of Surveillance Video." Applied Sciences 9, no. 10 (May 16, 2019): 2003. http://dx.doi.org/10.3390/app9102003.

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Intelligent analysis of surveillance videos over networks requires high recognition accuracy by analyzing good-quality videos that however introduce significant bandwidth requirement. Degraded video quality because of high object dynamics under wireless video transmission induces more critical issues to the success of smart video surveillance. In this paper, an object-based source coding method is proposed to preserve constant quality of video streaming over wireless networks. The inverse relationship between video quality and object dynamics (i.e., decreasing video quality due to the occurrence of large and fast-moving objects) is characterized statistically as a linear model. A regression algorithm that uses robust M-estimator statistics is proposed to construct the linear model with respect to different bitrates. The linear model is applied to predict the bitrate increment required to enhance video quality. A simulated wireless environment is set up to verify the proposed method under different wireless situations. Experiments with real surveillance videos of a variety of object dynamics are conducted to evaluate the performance of the method. Experimental results demonstrate significant improvement of streaming videos relative to both visual and quantitative aspects.

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De Meneses, Y. L., P. Roduit, F. Luisier, and J. Jacot. "Trajectory Analysis for Sport and Video Surveillance." ELCVIA Electronic Letters on Computer Vision and Image Analysis 5, no. 3 (November 1, 2005): 148. http://dx.doi.org/10.5565/rev/elcvia.113.

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Taha, Ahmed, Hala H. Zayed, M. E. Khalifa, and El-Sayed M. El-Horbaty. "Exploring Behavior Analysis in Video Surveillance Applications." International Journal of Computer Applications 93, no. 14 (May 16, 2014): 22–32. http://dx.doi.org/10.5120/16283-6045.

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Zhu, Tao, and Wei Jun Hong. "Effect Evaluation of Video Surveillance System on the Basis of Principal Component Analysis." Applied Mechanics and Materials 713-715 (January 2015): 479–81. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.479.

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The effect evaluation of video surveillance system is important for the effect of expected protection on the system. A comprehensive effect evaluation index system of video surveillance system is established. The Principal Component Analysis (PCA) method is applied on the established index system to obtain a new evaluation index system. It is proved in instances that the effect evaluation method of video surveillance system with the application of the index system is capable of evaluating the video surveillance system effectively and quantitatively. The protective effect of the video surveillance system is evaluated objectively on the basis of the new index system with the PCA.

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Xie, Feng, and Zheng Xu. "Semantic Based Annotation for Surveillance Big Data Using Domain Knowledge." International Journal of Cognitive Informatics and Natural Intelligence 9, no. 1 (January 2015): 16–29. http://dx.doi.org/10.4018/ijcini.2015010102.

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Video surveillance technology is playing a more and more important role in traffic detection. Vehicle's static properties are crucial information in examining criminal and traffic violations. Image and video resources play an important role in traffic events analysis. With the rapid growth of the video surveillance devices, large number of image and video resources is increasing being created. It is crucial to explore, share, reuse, and link these multimedia resources for better organizing traffic events. With the development of Video Surveillance technology, it has been wildly used in the traffic monitoring. Therefore, there is a trend to use Video Surveillance to do intelligent analysis on vehicles. Now, using software and tools to analyze vehicles in videos has already been used in smart cards and electronic eye, which helps polices to extract useful information like plate, speed, etc. And the key technology is to obtain various properties of the vehicle. This paper provides an overview of the algorithms and technologies used in extracting static properties of vehicle in the video.

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WARNICK, BRYAN. "Surveillance Cameras in Schools: An Ethical Analysis." Harvard Educational Review 77, no. 3 (September 1, 2007): 317–43. http://dx.doi.org/10.17763/haer.77.3.r2k76507rrjw8238.

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In this essay, Bryan R. Warnick responds to the increasing use of surveillance cameras in public schools by examining the ethical questions raised by their use. He explores the extent of a student's right to privacy in schools, stipulates how video surveillance is similar to and different from commonly accepted in-person surveillance practices, and discusses the possible impact of surveillance technology on educational environments. In response to the ethical concerns he raises, Warnick offers five suggestions for how schools can use video surveillance technology in more ethically sensitive ways.

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Bhushanam, M. V. Naga. "Real Time Night Vision Surveillance using Improved Dark Channel Prior." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 20, 2021): 1581–90. http://dx.doi.org/10.22214/ijraset.2021.35279.

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Videos taken under low lighting conditions usually result in severe loss of visibility and contrast and are uncomfortable for observation and analysis. Night vision cameras that cater to the needs are expensive and less versatile. To be cost effective and extract maximum information from videos taken in low lit conditions, video enhancing techniques must be used. Though there are many night vision enhancement techniques available in literature, this paper particularly emphasizes about Improved Dark Channel Prior algorithm and its results. This approach suits well for real time night video enhancement. It has been found that a pixel-wise inversion of a night video appears very similar to the video obtained during foggy days. The same idea of haze removal approach is used to boost the visual quality of night videos. An improved dark channel prior model is presented that is integrated with Gaussian Pyramid operators for local smoothing. The experimental results show that the proposed method can boost the perceptual quality of detailing in night videos.

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Abdullahi, Anas, Mustapha Aminu Bagiwa, Abubakar Roko, and Samaila Buda. "An Inter-Frame Forgery Detection Technique for Surveillance Videos Based on Analysis of Similarities." SLU Journal of Science and Technology 4, no. 1&2 (August 20, 2022): 15–26. http://dx.doi.org/10.56471/slujst.v4i.265.

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Background: In video forgeries, the insertion, duplication and deletion of frames are the most common forgeries that are used by attackers to alter targeted videos for malicious intent. Researchers have proposed the use of active and passive technologies for detecting video forgeries over the years. Active approaches are used to detect the occurrence of alterations in digital video with the use of embedded features such as digital signature and watermarks. However, techniques that are based on active approaches are only applicable to specialized hardware devices. A passive technique, on the other hand, detects forgery using the behavioral cues encoded in a video. In this paper, a passive video forgery detection system based on frame similarity analysis is presented.Inter frame forgeries (Insertion, Deletion, and Duplication) were detected using the proposed technique, which was unaffected by scene changes.The technique has the overall performance of 98.07% precision, 100% recall and 99.01% accuracy.

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Muhammad, Khan, Mohammad S. Obaidat, Tanveer Hussain, Javier Del Ser, Neeraj Kumar, Mohammad Tanveer, and Faiyaz Doctor. "Fuzzy Logic in Surveillance Big Video Data Analysis." ACM Computing Surveys 54, no. 3 (June 2021): 1–33. http://dx.doi.org/10.1145/3444693.

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CCTV cameras installed for continuous surveillance generate enormous amounts of data daily, forging the term Big Video Data (BVD). The active practice of BVD includes intelligent surveillance and activity recognition, among other challenging tasks. To efficiently address these tasks, the computer vision research community has provided monitoring systems, activity recognition methods, and many other computationally complex solutions for the purposeful usage of BVD. Unfortunately, the limited capabilities of these methods, higher computational complexity, and stringent installation requirements hinder their practical implementation in real-world scenarios, which still demand human operators sitting in front of cameras to monitor activities or make actionable decisions based on BVD. The usage of human-like logic, known as fuzzy logic, has been employed emerging for various data science applications such as control systems, image processing, decision making, routing, and advanced safety-critical systems. This is due to its ability to handle various sources of real-world domain and data uncertainties, generating easily adaptable and explainable data-based models. Fuzzy logic can be effectively used for surveillance as a complementary for huge-sized artificial intelligence models and tiresome training procedures. In this article, we draw researchers’ attention toward the usage of fuzzy logic for surveillance in the context of BVD. We carry out a comprehensive literature survey of methods for vision sensory data analytics that resort to fuzzy logic concepts. Our overview highlights the advantages, downsides, and challenges in existing video analysis methods based on fuzzy logic for surveillance applications. We enumerate and discuss the datasets used by these methods, and finally provide an outlook toward future research directions derived from our critical assessment of the efforts invested so far in this exciting field.

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Javanbakhti, Solmaz, Sveta Zinger, and Peter H. N. De With. "Fast scene analysis for surveillance & video databases." IEEE Transactions on Consumer Electronics 63, no. 3 (August 2017): 325–33. http://dx.doi.org/10.1109/tce.2017.014979.

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Agrawal, Priyanka. "Smart Surveillance System using Face Tracking." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 25, 2021): 2613–17. http://dx.doi.org/10.22214/ijraset.2021.35567.

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The face is seen as a key component of the human body, and humans utilise it to identify one another. Face detection in video refers to the process of detecting a person's face from a video sequence, while face tracking refers to the process of tracking the person's face throughout the video. Face detection and tracking has become a widely researched issue due to applications such as video surveillance systems and identifying criminal activity. However, working with videos is tough due to problems such as bad illumination, low resolution, and atypical posture, among others. It is critical to produce a fair analysis of various tracking and detection strategies in order to fulfil the goal of video tracking and detection. Closed-circuit television (CCTV) technology had a significant impact on how crimes were investigated and solved. The material used to review crime scenes was CCTV footage. CCTV systems, on the other hand, just offer footage and do not have the ability to analyse it. In this research, we propose a system that can be integrated with the CCTV footage or any other video input like webcam to detect, recognise, and track a person of interest. Our system will follow people as they move through a space and will be able to detect and recognise human faces. It enables video analytics, allowing existing cameras to be combined with a system that will recognise individuals and track their activities over time. It may be used for remote surveillance and can be integrated into video analytics software and CCTV security solutions as a component. It may be used on college campuses, in offices, and in shopping malls, among other places.

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Sanyanga, Tapiwanashe Miranda, Munyaradzi Sydney Chinzvende, Tatenda Duncan Kavu, and John Batani. "Searching Objects in a Video Footage." International Journal of ICT Research in Africa and the Middle East 8, no. 2 (July 2019): 18–31. http://dx.doi.org/10.4018/ijictrame.2019070102.

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Due to the increase in video content being generated from surveillance cameras and filming, videos analysis becomes imperative. Sometimes it becomes tedious to watch a video captured by a surveillance camera for hours, just to find out the desired footage. Current state of-the-art video analysis methods do not address the problem of searching and localizing a particular object in a video using the name of the object as a query and to return only a segment of the video clip showing the instances of that object. In this research the authors make use of combined implementations from existing work and also applied the dropping frames algorithm to produce a shorter, trimmed video clip showing the target object specified by the search tag. The resulting video is short and specific to the object of interest.

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Simbirsky, Gennady. "Analysis of methods for detection of anomalies in video series of video surveillance camera on vehi-cles." Bulletin of Kharkov National Automobile and Highway University, no. 98 (November 29, 2022): 26. http://dx.doi.org/10.30977/bul.2219-5548.2022.98.0.26.

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Problem. Video surveillance is a process of monitoring various objects, which is implemented with the use of video cameras - optical-electronic and microprocessor devices, designed for visual control of the environment, with the aim of the safety of life, activity and property of a modern person. Such processes and objects can be, for example, cars moving at an intersection, on a street or on a country road, a road surface during the control of its condition and quality, a security system of any infrastructure object. Goal. The purpose of the study is the analysis of the technical composition of systems for detecting anomalies in the video of video surveillance cameras and a comparative review of computational methods for processing the results of this observation. To achieve the goal, it is necessary to research literary sources, that is, articles in scientific journals, reports at conferences, articles on non-thematic web portals, monographs and textbooks, the names of which indicate the possibility of finding information useful for this research. Methodology. As part of the research task, we are interested in the technologies, systems and methods that have been proposed and developed for obtaining, processing and analyzing video sequences and images, including machine vision tasks, image classification, object and anomaly detection, image segmentation, etc. Results. As a result of this research, the following was done: 1) An overview of the main modern systems for detecting anomalies in the video series of video surveillance cameras was conducted. It was concluded that the differences between the anomaly detection systems in the video series of video surveillance cameras are due to the choice of methods for processing video information. 2) An analysis of methods of detecting anomalies in the video series of video surveillance cameras was carried out. For this purpose, a classification of modern methods of detecting anomalies in the video series was developed and the basics of the theory of deep neural networks were considered in terms of the possibility of their application for classification, localization, segmentation, detection, identification and tracking of objects in the video series of surveillance cameras. Originality. An overview of the main modern systems for detecting anomalies in the video series of video surveillance cameras was conducted. It was concluded that the differences between the systems for searching for anomalies in the video series of video surveillance cameras are determined by the choice of methods for processing video information. An analysis of the methods of detecting anomalies in the video series of video surveillance cameras was carried out. Practical value. The developed information system is already used to provide students of all educational institutions of Ukraine of the III level of accreditation with the information about our university; regarding the specialties offered by the university and the corresponding professions; regarding open days, preparatory courses and much more.

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NASROLLAHI, KAMAL, THOMAS B. MOESLUND, and MOHAMMAD RAHMATI. "SUMMARIZATION OF SURVEILLANCE VIDEO SEQUENCES USING FACE QUALITY ASSESSMENT." International Journal of Image and Graphics 11, no. 02 (April 2011): 207–33. http://dx.doi.org/10.1142/s0219467811004068.

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Constant working surveillance cameras in public places, such as airports and banks, produce huge amount of video data. Faces in such videos can be extracted in real time. However, most of these detected faces are either redundant or useless. Redundant information adds computational costs to facial analysis systems and useless data makes the final results of such systems noisy, unstable, and erroneous. Thus, there is a need for a mechanism to summarize the original video sequence to a set of the most expressive images of the sequence. The proposed system in this paper uses a face quality assessment technique for this purpose. The summarized results of this technique have been used in three different facial analysis systems and the experimental results on real video sequences are promising.

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Li, Hao, Tianhao Xiezhang, Cheng Yang, Lianbing Deng, and Peng Yi. "Secure Video Surveillance Framework in Smart City." Sensors 21, no. 13 (June 28, 2021): 4419. http://dx.doi.org/10.3390/s21134419.

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In the construction process of smart cities, more and more video surveillance systems have been deployed for traffic, office buildings, shopping malls, and families. Thus, the security of video surveillance systems has attracted more attention. At present, many researchers focus on how to select the region of interest (RoI) accurately and then realize privacy protection in videos by selective encryption. However, relatively few researchers focus on building a security framework by analyzing the security of a video surveillance system from the system and data life cycle. By analyzing the surveillance video protection and the attack surface of a video surveillance system in a smart city, we constructed a secure surveillance framework in this manuscript. In the secure framework, a secure video surveillance model is proposed, and a secure authentication protocol that can resist man-in-the-middle attacks (MITM) and replay attacks is implemented. For the management of the video encryption key, we introduced the Chinese remainder theorem (CRT) on the basis of group key management to provide an efficient and secure key update. In addition, we built a decryption suite based on transparent encryption to ensure the security of the decryption environment. The security analysis proved that our system can guarantee the forward and backward security of the key update. In the experiment environment, the average decryption speed of our system can reach 91.47 Mb/s, which can meet the real-time requirement of practical applications.

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Aparna, RR. "Swarm Intelligence for Automatic Video Image Contrast Adjustment." International Journal of Rough Sets and Data Analysis 3, no. 3 (July 2016): 21–37. http://dx.doi.org/10.4018/ijrsda.2016070102.

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Video surveillance has become an integrated part of today's life. We are surrounded by video cameras in all the public places and organizations in our day to day life. Many useful information like face detection, traffic analysis, object classification, crime analysis can be assessed from the recorded videos. Image enhancement plays a vital role to extract any useful information from the images. Enhancing the video frames is a major part as it serves the further analysis of video sequences. The proposed paper discusses the automatic contrast adjustment in the video frames. A new hybrid algorithm was developed using the spatial domain method and Artificial Bee Colony Algorithm (ABC), a swarm intelligence based technique for image enhancement. The proposed algorithm was tested using the traffic surveillance images. The proposed method produced good results and better quality picture for varied levels of poor quality video frames.

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Chen, Qi, Li Yang, Dongping Zhang, Ye Shen, and Shuying Huang. "Face Deduplication in Video Surveillance." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 03 (November 22, 2017): 1856001. http://dx.doi.org/10.1142/s0218001418560013.

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The video surveillance system based on face analysis has played an increasingly important role in the security industry. Compared with identification methods of other physical characteristics, face verification method is easy to be accepted by people. In the video surveillance scene, it is common to capture multiple faces belonging to a same person. We cannot get a good result of face recognition if we use all the images without considering image quality. In order to solve this problem, we propose a face deduplication system which is combined with face detection and face quality evaluation to obtain the highest quality face image of a person. The experimental results in this paper also show that our method can effectively detect the faces and select the high-quality face images, so as to improve the accuracy of face recognition.

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Chebi, Hocine. "A New Modeling Approach for the Video Pre-Analysis of Video Surveillance Systems." International Journal of Applied Evolutionary Computation 12, no. 3 (July 2021): 21–34. http://dx.doi.org/10.4018/ijaec.2021070102.

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The work presented in this paper aims to develop a new architecture for video surveillance systems. Among the problems encountered when tracking and classifying objects are groups of occluded objects. Simplifying the representation of objects leads to other reliable object tracking with smaller amounts of information used but protection of the necessary characteristics. Therefore, modeling moving objects into a simpler form can be considered a pre-analysis technique. Objects can be represented in different ways, and the choice of the representation of an object strongly depends on the field of application. An example of a video surveillance system respecting this architecture and using the pre-analysis method is proposed.

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Kong, Lingchao, and Rui Dai. "Efficient Video Encoding for Automatic Video Analysis in Distributed Wireless Surveillance Systems." ACM Transactions on Multimedia Computing, Communications, and Applications 14, no. 3 (August 31, 2018): 1–24. http://dx.doi.org/10.1145/3226036.

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Chen, Wei Bing, Fei Jang Huang, Gang Lin Zhang, and Zhu Xian Zhang. "The Analysis and Review of Mobile Surveillance Video Based on AVS-S." Advanced Materials Research 546-547 (July 2012): 634–39. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.634.

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In order to realize digitizing and networking video surveillance, a kind of excellent technology of video compression is need. In this paper, the audio video coding standard based on AVS-S was systematically introduced, including its development history, target requirements, technical features and existing algorithms. All possible facing problems when AVS-S is applied in mobile video surveillance systems were analyzed. The corresponding solutions were also provided. Especially, how to use intelligent video technology to satisfy with even higher security-monitoring targets was discussed.

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Muhammed Anees, V., and G. Santhosh Kumar. "Identification of crowd behaviour patterns using stability analysis." Journal of Intelligent & Fuzzy Systems 42, no. 4 (March 4, 2022): 2829–43. http://dx.doi.org/10.3233/jifs-200667.

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Crowd behaviour analysis and management have become a significant research problem for the last few years because of the substantial growth in the world population and their security requirements. There are numerous unsolved problems like crowd flow modelling and crowd behaviour detection, which are still open in this area, seeking great attention from the research community. Crowd flow modelling is one of such problems, and it is also an integral part of an intelligent surveillance system. Modelling of crowd flow has now become a vital concern in the development of intelligent surveillance systems. Real-time analysis of crowd behavior needs accurate models that represent crowded scenarios. An intelligent surveillance system supporting a good crowd flow model will help identify the risks in a wide range of emergencies and facilitate human safety. Mathematical models of crowd flow developed from real-time video sequences enable further analysis and decision making. A novel method identifying eight possible crowd flow behaviours commonly seen in the crowd video sequences is explained in this paper. The proposed method uses crowd flow localisation using the Gunnar-Farneback optical flow method. The Jacobian and Hessian matrix analysis along with corresponding eigenvalues helps to find stability points identifying the flow patterns. This work is carried out on 80 videos taken from UCF crowd and CUHK video datasets. Comparison with existing works from the literature proves our method yields better results.

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Makovoz, K. O. "Intrusion detection methods in cloud video surveillance systems." CTE Workshop Proceedings 1 (March 21, 2013): 53. http://dx.doi.org/10.55056/cte.87.

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Video surveillance systems are a set of equipment and software that is used to organize video surveillance at sites, regardless of their territorial location.Cloud infrastructure can be effectively used to scale a video surveillance system in the following dimensions:- storing video and video analytics metadata;- connection of new surveillance objects (e.g., retail outlets);- implementation of new functions of metadata analysis and archive search;-servicing a large number of users.

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Zhang, Chengcui. "A Survey of Visual Traffic Surveillance Using Spatio-Temporal Analysis and Mining." International Journal of Multimedia Data Engineering and Management 4, no. 3 (July 2013): 42–60. http://dx.doi.org/10.4018/jmdem.2013070103.

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The focus of this survey is on spatio-temporal data mining and database retrieval for visual traffic surveillance systems. In many traffic surveillance applications, such as incident detection, abnormal events detection, vehicle speed estimation, and traffic volume estimation, the data used for reasoning is really in the form of spatio-temporal data (e.g. vehicle trajectories). How to effectively analyze these spatio-temporal data to automatically find its inherent characteristics for different visual traffic surveillance applications has been of great interest. Examples of spatio-temporal patterns extracted from traffic surveillance videos include, but are not limited to, sudden stops, harsh turns, speeding, and collisions. To meet the different needs of various traffic surveillance applications, several application- or event- specific models have been proposed in the literature. This paper provides a survey of different models and data mining algorithms to cover state of the art in spatio-temporal modelling, spatio-temporal data mining, and spatio-temporal retrieval for traffic surveillance video databases. In addition, the database model issues and challenges for traffic surveillance videos are also discussed in this survey.

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Socha, Robert, and Bogusław Kogut. "Urban Video Surveillance as a Tool to Improve Security in Public Spaces." Sustainability 12, no. 15 (August 1, 2020): 6210. http://dx.doi.org/10.3390/su12156210.

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Video surveillance is an integral part of the contemporary world. Its use increases the sense of security but also generates certain risks. Laws do not always clearly and comprehensively define the rules for installing and using video surveillance and different rules are adopted in different countries to address these issues. This article presents an analysis of statistical data concerning urban video surveillance as a tool to improve the security of public spaces in the city of Katowice using the example of the operation of the Katowice Smart Surveillance and Analysis System. By presenting the operation of video surveillance in two different time periods, it was possible to assess the effectiveness of urban video surveillance for the security of public spaces in terms of particularly onerous crimes. The technical and organizational solutions applied, as in the case of the Katowice Smart Surveillance and Analysis System, made it possible to assess the impact of the operation of the system on offenses and the number of legal proceedings.

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Ullrich, Peter, and Philipp Knopp. "Protesters’ Reactions to Video Surveillance of Demonstrations: Counter-Moves, Security Cultures, and the Spiral of Surveillance and Counter-Surveillance." Surveillance & Society 16, no. 2 (July 14, 2018): 183–202. http://dx.doi.org/10.24908/ss.v16i2.6823.

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This article analyses protesters’ reactions to police video surveillance of demonstrations in Germany. Theoretically, we draw on the concept of a “spiral of surveillance and counter-surveillance” to understand the interaction processes which—intentionally or not—contribute to the deepening of the “surveillant assemblage” in the field of protest policing. After introducing video surveillance and its importance for selective protest policing, we discuss concepts of counter-surveillance. Widening the individualist scope of former research on “neutralisation techniques,” collective and interactive dimensions are added to cover the full counter-surveillance repertoire. We identified six basic categories of counter-surveillance moves: consider cameras, disguise, attack, hide, sousveillance, and cooperation. They can be classified along the axes of (a) degree of cooperation with the police, and (b) directedness (inwards/outward). It becomes obvious that activists are not predominantly deterred by video surveillance but adapt to the situation. If and how certain counter-surveillance moves are applied depends on the degree of exposure, perceptions of conflict dynamics, political interpretations, and on how these factors are processed in the respective security cultures. Security cultures, which are grounded in the respective relations between protest groups and police, are collective sets of practices and interpretive patterns aimed at securing safety and/or anonymity of activists as well as making their claims visible. Thus, they are productive power effects, resulting from the very conditions under which protest takes place in contemporary surveillance societies. This article elaborates on these ambiguities and unintended effects with regard to sousveillance and disguise techniques, such as masking or uniform clothing. The analysis is based on qualitative data collected between 2011 and 2016 consisting of group discussions and interviews with activists from different political spectra, journalists, politicians, and police officers, as well as observations of demonstrations and document analyses of movement literature.

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Liao, W., C. Yang, M. Ying Yang, and B. Rosenhahn. "SECURITY EVENT RECOGNITION FOR VISUAL SURVEILLANCE." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-1/W1 (May 30, 2017): 19–26. http://dx.doi.org/10.5194/isprs-annals-iv-1-w1-19-2017.

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With rapidly increasing deployment of surveillance cameras, the reliable methods for automatically analyzing the surveillance video and recognizing special events are demanded by different practical applications. This paper proposes a novel effective framework for security event analysis in surveillance videos. First, convolutional neural network (CNN) framework is used to detect objects of interest in the given videos. Second, the owners of the objects are recognized and monitored in real-time as well. If anyone moves any object, this person will be verified whether he/she is its owner. If not, this event will be further analyzed and distinguished between two different scenes: moving the object away or stealing it. To validate the proposed approach, a new video dataset consisting of various scenarios is constructed for more complex tasks. For comparison purpose, the experiments are also carried out on the benchmark databases related to the task on abandoned luggage detection. The experimental results show that the proposed approach outperforms the state-of-the-art methods and effective in recognizing complex security events.

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Zhang, Kai. "Intelligent Video Analysis System Based on ARM Cortex." Applied Mechanics and Materials 556-562 (May 2014): 3216–18. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3216.

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With the development of the society, security monitoring system more and more get people's attention. Article designs the intelligent video analysis technology based on ARM architecture on the basis of Intelligent Video Surveillance, and discusses the application prospect of intelligent video analysis system.

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Bello, Oladipupo. "EMPIRICAL INVESTIGATION OF VIDEO SURVEILLANCE SYSTEM ADOPTION IN NIGERIA." International Journal of Engineering Applied Sciences and Technology 6, no. 8 (December 1, 2021): 12–19. http://dx.doi.org/10.33564/ijeast.2021.v06i08.002.

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—The acceptance of video surveillance systems at homes, offices and business enterprises nowadays to reduce incidences of theft, burglaries, sexual offences and other forms of crimes is increasing. This study investigated the factors that influence adoption of video surveillance systems in Nigeria. Based on Rogers’ diffusion of innovation model, the effect of relative advantage, compatibility, complexity, trialability and observability on adoption of video surveillance systems was examined. Structural equation model analysis of the collected data shows that relative advantage, compatibility, trialability and observability significantly influence adoption of video surveillance systems. However, the results indicate that adoption of video surveillance systems is not influenced significantly by complexity. Thus, complexity of video surveillance systems is perceived as a barrier to adoption of this technological innovation among Nigerians.

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Anishchenko, L. N., S. I. Ivashov, and A. V. Skrebkov. "INTELLIGENT VIDEO ANALYSIS OF DANGEROUS SITUATIONS." World of Transport and Transportation 15, no. 6 (December 28, 2017): 182–93. http://dx.doi.org/10.30932/1992-3252-2017-15-6-18.

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[For the English abstract and full text of the article please see the attached PDF-File (English version follows Russian version)].The work was supported by the Russian Foundation for Basic Research (Grant No. 17-20-03034). ABSTRACT The article is devoted to development of a system for the intelligent analysis of video recordings of external surveillance cameras, which makes it possible to identify dangerous situations at railway facilities using the example of detection of falls in the track area. A method of preprocessing a video for the purpose of forming a feature space based on the use of background subtraction using the Gaussian mixture method, followed by tracking the movement of a person with the help of the Kalman filter and deformation of the shape of the mobile object as a result of applying the procrustean analysis is proposed. The selection of the optimal composition of the feature space and additional heuristics providing the isolation of episodes of falls from video recording with an average quality of the Cohen’s kappa 0,62 is compared with the visual analysis by the operator. Keywords: railway, safety, video surveillance, intelligent video analysis, motion recognition, machine learning, form analysis.

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Przybylo, Jaromir, Joanna Grabska-Chrzastowska, and Przemyslaw Korohoda. "Low-Cost Scalable Home Video Surveillance System." Image Processing & Communications 19, no. 2-3 (September 1, 2014): 51–58. http://dx.doi.org/10.1515/ipc-2015-0010.

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Abstract Automated and intelligent video processing and analysis systems are becoming increasingly popular in video surveillance. Such systems must meet a number of requirements, such as threat detection and real-time video recording. Furthermore, they cannot be expensive and must not consume too much energy because they have to operate continuously. The work presented here focuses on building a home video surveillance system matching the household budget and possibly making use of hardware available in the house. Also, it must provide basic functionality (such as video recording and detecting threats) all the time, and allow for a more in-depth analysis when more computing power be available.

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Ivanov, Yurii, Borys Sharov, Nazar Zalevskyi, and Ostap Kernytskyi. "Software System for End-Products Accounting in Bakery Production Lines Based on Distributed Video Streams Analysis." Advances in Cyber-Physical Systems 7, no. 2 (December 16, 2022): 101–7. http://dx.doi.org/10.23939/acps2022.02.101.

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Among the main requirements of modern surveillance systems are stability in the face of negative influences and intellectualization. The purpose of intellectualization is that the surveillance system should perform not only the main functions such as monitoring and stream recording but also have to provide effective stream processing. The requirement for this processing is that the system operation has to be automated, and the operator's influence should be minimal. Modern intelligent surveillance systems require the development of grouping methods. The context of the grouping method here is associated with a decomposition of the target problem. Depending on the purpose of the system, the target problem can represent several subproblems, each of which usually accomplishes by artificial intelligence or data mining methods.

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Lalitha, R. V. S., Divya Lalita Sri Jalligampala, Kayiram Kavitha, Shaik Vahida, and Goli Rajasekhar. "New Directions in Traffic Control Analysis through Video Surveillance." E3S Web of Conferences 309 (2021): 01099. http://dx.doi.org/10.1051/e3sconf/202130901099.

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Traffic management is an increasing problem in both cities and sub urban areas. Authority people involved in traffic management system spend much of time in controlling traffic at junctions. With the advances in technology, monitoring traffic through image processing and video surveillance techniques became the researchers’ attention. These techniques help us in controlling traffic as well as to identification of kamikaze drivers and speed violators. The key focus of this research is to do traffic analysis using video surveillance to detect speedy drivers. A wide range of traffic parameters such as flow of traffic, speed of vehicles and vehicle registration number are the major components involved in this research. In this paper, traffic analysis is carried out based on streaming video data with YOLO tool. In this paper an eco system is developed for object detection, vehicle number detection and the speed of the vehicle using computer vision algorithms. With the application tool developed, traffic control authority people can warn the speedy drivers on the fly.

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Lao, Weilun, Jungong Han, and Peter H. N. de With. "Flexible Human Behavior Analysis Framework for Video Surveillance Applications." International Journal of Digital Multimedia Broadcasting 2010 (2010): 1–9. http://dx.doi.org/10.1155/2010/920121.

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We study a flexible framework for semantic analysis of human motion from surveillance video. Successful trajectory estimation and human-body modeling facilitate the semantic analysis of human activities in video sequences. Although human motion is widely investigated, we have extended such research in three aspects. By adding a second camera, not only more reliable behavior analysis is possible, but it also enables to map the ongoing scene events onto a 3D setting to facilitate further semantic analysis. The second contribution is the introduction of a 3D reconstruction scheme for scene understanding. Thirdly, we perform a fast scheme to detect different body parts and generate a fitting skeleton model, without using the explicit assumption of upright body posture. The extension of multiple-view fusion improves the event-based semantic analysis by 15%–30%. Our proposed framework proves its effectiveness as it achieves a near real-time performance (13–15 frames/second and 6–8 frames/second) for monocular and two-view video sequences.

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Kapoor, Surbhi. "An Intelligent Video Surveillance System: Moving Object Behavior Analysis." Indian Journal of Science and Technology 9, no. 1 (January 20, 2016): 1–16. http://dx.doi.org/10.17485/ijst/2016/v9i47/106900.

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Mukherjee, Anupam. "Motion analysis in video surveillance using edge detection techniques." IOSR Journal of Computer Engineering 12, no. 6 (2013): 10–15. http://dx.doi.org/10.9790/0661-1261015.

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Kang, Joohyung, and Sooyeong Kwak. "Violent Behavior Detection using Motion Analysis in Surveillance Video." Journal of Broadcast Engineering 20, no. 3 (May 30, 2015): 430–39. http://dx.doi.org/10.5909/jbe.2015.20.3.430.

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Makdissi, Michael, and Gavin Davis. "Using video analysis for concussion surveillance in Australian football." Journal of Science and Medicine in Sport 19, no. 12 (December 2016): 958–63. http://dx.doi.org/10.1016/j.jsams.2016.02.014.

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WU, YOUFU, and MO DAI. "DETECTION AND ANALYSIS OF MOVING OBJECTS FOR VIDEO SURVEILLANCE." International Journal of Information Acquisition 02, no. 03 (September 2005): 227–39. http://dx.doi.org/10.1142/s0219878905000623.

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In this paper, we address the problem of detection and analysis of moving objects in a video stream obtained by a fixed camera. To detect the moving objects, the tradition method is to create a fixed image first, which includes all the motionless parts of the scene, known as the background model. The difficulty of this approach lies mainly in two aspects: The first relates to the fact that a slow moving object can leave a visible trace in background model. The latter comes from the variation of illumination in the course of time so it cannot obtain a reasonable background model. To overcome these difficulties, we propose a multiple background model. At the exit of the detection of moving objects, the tracking (matching) of a moving object extracted in the successive images is necessary to analyze its behavior. After the matching of mobile objects, a series of analysis methods are presented. The proposed tracking and analysis methods allow dealing with partial occlusions, stopping and going motion, moving directions, crossing of moving object in very challenging situations. The experiment and comparison results are reported for different real sequences, which show better performance of our methods.

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Jiang, Hong, Songqing Zhao, Zuowei Shen, Wei Deng, Paul A. Wilford, and Raziel Haimi-Cohen. "Surveillance video analysis using compressive sensing with low latency." Bell Labs Technical Journal 18, no. 4 (March 2014): 63–74. http://dx.doi.org/10.1002/bltj.21646.

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Huang, Shih-Chia. "An Advanced Motion Detection Algorithm With Video Quality Analysis for Video Surveillance Systems." IEEE Transactions on Circuits and Systems for Video Technology 21, no. 1 (January 2011): 1–14. http://dx.doi.org/10.1109/tcsvt.2010.2087812.

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Komorkiewicz, Mateusz, and Jaromir Przybyło. "Pedestrian Detection and Analysis with Scale-Space and Distance Transform." Image Processing & Communications 17, no. 4 (December 1, 2012): 283–95. http://dx.doi.org/10.2478/v10248-012-0057-4.

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Abstract Because the amount of various video streams recorded by video surveillance systems is increasing, the new approach, where human operator analyzing the video is replaced by artificial intelligence system is gaining new followers. The algorithm have to meet several requirements: must be accurate and not produce too many false alarms, moreover it must be able to process the received video stream in real-time to provide sufficient response time. In the article a system is presented which is able to detect and analyze walking pedestrians. It is based on two algorithms: scale space and matching contours using distance transform. The information can be used by other parts of the advanced video surveillance system, namely object tracking by detection, detecting heavy equipment only zone intrusion or for sorting out possible suspicious persons (pickpocket, homeless etc.).

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Sun, Yanjie, Mingguang Wu, Xiaoyan Liu, and Liangchen Zhou. "High-Precision Dynamic Traffic Noise Mapping Based on Road Surveillance Video." ISPRS International Journal of Geo-Information 11, no. 8 (August 4, 2022): 441. http://dx.doi.org/10.3390/ijgi11080441.

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High-precision dynamic traffic noise maps can describe the spatial and temporal distributions of noise and are necessary for actual noise prevention. Existing monitoring point-based methods suffer from limited spatial adaptability, and prediction model-based methods are limited by the requirements for traffic and environmental parameter specifications. Road surveillance video data are effective for computing and analyzing dynamic traffic-related factors, such as traffic flow, vehicle speed and vehicle type, and environmental factors, such as road material, weather and vegetation. Here, we propose a road surveillance video-based method for high-precision dynamic traffic noise mapping. First, it identifies dynamic traffic elements and environmental elements from videos. Then, elements are converted from image coordinates to geographic coordinates by video calibration. Finally, we formalize a dynamic noise mapping model at the lane level. In an actual case analysis, the average error is 1.53 dBA. As surveillance video already has a high coverage rate in most cities, this method can be deployed to entire cities if needed.

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Huang, Shaonian, Dongjun Huang, and Mansoor Ahmed Khuhro. "Crowd Motion Analysis Based on Social Force Graph with Streak Flow Attribute." Journal of Electrical and Computer Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/492051.

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Over the past decades, crowd management has attracted a great deal of attention in the area of video surveillance. Among various tasks of video surveillance analysis, crowd motion analysis is the basis of numerous subsequent applications of surveillance video. In this paper, a novel social force graph with streak flow attribute is proposed to capture the global spatiotemporal changes and the local motion of crowd video. Crowd motion analysis is hereby implemented based on the characteristics of social force graph. First, the streak flow of crowd sequence is extracted to represent the global crowd motion; after that, spatiotemporal analogous patches are obtained based on the crowd visual features. A weighted social force graph is then constructed based on multiple social properties of crowd video. The graph is segmented into particle groups to represent the similar motion patterns of crowd video. A codebook is then constructed by clustering all local particle groups, and consequently crowd abnormal behaviors are detected by using the Latent Dirichlet Allocation model. Extensive experiments on challenging datasets show that the proposed method achieves preferable results in the application of crowd motion segmentation and abnormal behavior detection.

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Chmiel, Wojciech, Joanna Kwiecień, and Zbigniew Mikrut. "Realization of Scenarios for Video Surveillance." Image Processing & Communications 17, no. 4 (December 1, 2012): 231–40. http://dx.doi.org/10.2478/v10248-012-0051-x.

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Abstract The design of a methodology for the effective scene understanding systems is one of the main goals of the researchers in the analysis of video surveillance. The objects in the scene have to be identified. Hence, it is necessary to detect the parts belonging to the background. In the article we introduce the base algorithms, which enable us to realization of scenarios. We briefly describe base algorithms (object detection, object localization, recognition of humans, movement detection and configuration of scene) used in three selected scenarios: violation of protected zones, abandoned objects and vandalism (graffiti). These scenarios were tested on several films, obtained from Internet and made by participants of project SIMPOZ. The results of our experiments are presented. The basic algorithms for detecting and locating objects are very quickly, but movement detection ("optical flow") and recognition of humans algorithms work longer.

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Marković, Jana. "Video surveillance in the function of crime prevention in open urban public space." Годишњак Факултета безбедности, no. 1 (2021): 31–49. http://dx.doi.org/10.5937/fb_godisnjak0-32977.

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The problem addressed in this paper is related to the role that video surveillance plays in preventing security breaches, and primarily in preventing crime in open urban public spaces. Therefore, the basic assumption is that video surveillance has a positive effect on the prevention and reduction of crime in the above-mentioned spaces - which are generally available to all, unlimited and free of charge, and which are "covered" by video surveillance. Based on the analysis of the content of relevant literature, the author researched and presented the relevant concepts of open urban public spaces, and pointed out key security issues, especially undesirable and/ or sanctioned acts that may endanger the security, public order and/or peace of these spaces. Further, the mechanisms for the prevention of security breaches were presented, and especially video surveillance conducted by public institutions and the private sector. The use of video surveillance brings both benefits and limitations that the author noticed, and presented as also an important goal of this paper. Considering all the above, the expected result of the work is a clear presentation of the role of video surveillance in open public urban spaces, and a clear distinction of what are the benefits and what are the limitations that this type of surveillance brings.

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Chung, Jen-Li, Lee-Yeng Ong, and Meng-Chew Leow. "Comparative Analysis of Skeleton-Based Human Pose Estimation." Future Internet 14, no. 12 (December 15, 2022): 380. http://dx.doi.org/10.3390/fi14120380.

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Human pose estimation (HPE) has become a prevalent research topic in computer vision. The technology can be applied in many areas, such as video surveillance, medical assistance, and sport motion analysis. Due to higher demand for HPE, many HPE libraries have been developed in the last 20 years. In the last 5 years, more and more skeleton-based HPE algorithms have been developed and packaged into libraries to provide ease of use for researchers. Hence, the performance of these libraries is important when researchers intend to integrate them into real-world applications for video surveillance, medical assistance, and sport motion analysis. However, a comprehensive performance comparison of these libraries has yet to be conducted. Therefore, this paper aims to investigate the strengths and weaknesses of four popular state-of-the-art skeleton-based HPE libraries for human pose detection, including OpenPose, PoseNet, MoveNet, and MediaPipe Pose. A comparative analysis of these libraries based on images and videos is presented in this paper. The percentage of detected joints (PDJ) was used as the evaluation metric in all comparative experiments to reveal the performance of the HPE libraries. MoveNet showed the best performance for detecting different human poses in static images and videos.

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Journal articles on the topic 'Surveillance video analysis'

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