Intelligent video surveillance for abnormal event detection

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Developing intelligent video surveillance systems that are able to automatically detect abnormal events requires solving challenging computer vision problems, such as object detection, tracking, and image recognition. During recent years, such problems have been addressed by increasingly more robust algorithms, producing impressive performances on standard benchmarks. However, this progress is not as impressive in real-world applications, where the vast majority of video surveillance systems still require human attention and manual intervention. For this reason, we argue that a significant gap exists between the work on fundamental problems of computer vision, and the development of intelligent video surveillance applications. The present research aims at narrowing this gap, by enhancing the intelligence of video surveillance systems. By intelligence, we mean to assist humans and reduce cognitive overload by extracting important information and identifying abnormal events automatically. Our long-term goal is thus to develop solutions to better understand monitored scenes, through the efficient exploitation of computer vision methods and visual acquisition technology. To reach this goal, we take an approach that starts by studying the limitations of state-of-the-art applications. This first step is crucial to detect priority problems and investigate relevant methodologies in order to develop robust video surveillance systems. Following this approach, we propose 2 short-term objectives. According to the fundamental axis of this proposal, we aim at (1) improving real-time object and person tracking, by making the optimal exploitation of deep learning and machine learning methods. More specifically, our work will be focused on visual object tracking (VOT) and human skeleton tracking, with the objective of increasing both tracking accuracy and speed. At the application level, we will take advantage of our work on the tracking problem for (2) detecting and analyzing abnormal events. We will address both outdoor and indoor application contexts, respectively, by exploring the use of a Pan-Tilt-Zoom (PTZ) camera to monitor moderately crowded scenes, and an RGB-D camera for skeleton-based behavior analysis. Our research program is innovative in several respects, as we study abnormal behaviors that are almost unexplored in a computer vision approach (e.g. suicide attempts), and we propose original methodologies that have not been investigated in previous works. We thus expect our work to produce highly cited research and practical techniques for intelligent video surveillance systems. Moreover, the program is specifically tailored for training HQPs. With the emergence of companies related to computer vision and machine learning, involved HQPs will acquire an expertise in high demand across Canada.