Intelligent surveillance for event detection

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Detecting and informing users about events of interest is the key function in intelligent video surveillance systems. Among the most important requirements is that the real-time surveillance system must: 1) operate continuously and autonomously in real-world conditions, and 2) be reliable without overloading the user with useless detection.These are the two specific problems that we are addressing in the proposed project, where we aim to develop an efficient video surveillance system that intelligently detects and reports events of interest to the user. This is a very challenging problem since the visual characteristics of an unconstrained scene are unstable, mainly due to large variations in illumination. On the other hand, the definition of an event of interest may vary between users. Currently, most of the existing systems use visible-light cameras and operate in constantly and evenly illuminated areas to detect simple events (e.g. intrusion), using basic change detection techniques. Some of the recent systems give the users the possibility to customize the detection by drawing interest zones and setting motion parameters such as the direction and velocity. But, this customization remains a complex and time-consuming process that most users are not willing to follow. Our investigation will therefore focus on designing methods and algorithms for detecting events of interest in the user`s context. The novelty of our approach is twofold. The first consists in using depth maps that can be obtained regardless of the illumination conditions, using recent cost effective RGB-D sensors. As a consequence, more robust appearance models can be constructed by adding 3D position information to standard color features. The second consists in enabling the user to rate the importance of a reported event. Therefore, detection would be improved by tuning the alerts for a given user based on his/ her past feedback. This project objectives include the development of: 1) models for actions and events in the 4D domain (3D space and time), 2) methods for recognizing actions and events, and 3) methods to learn from user feedback for future detection.