Content-based video analysis for visual surveillance and video interpretation

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The objective of this research is to develop new methods and algorithms for the analysis of video content. Our goal is to extract meaningful information from live video streams and from video recordings. The extracted information could be about the scene content and its structure or about the actions that are occurring. In particular, we will develop approaches to be used in the context of intelligent visual surveillance where events of interest must be reliably detected and classified. We will study the problem of the reliable long-term tracking of moving objects, under occlusions, and across cameras. Our strategy will be to build an environmental model of the observed scene containing structural information about the static elements and representation of the way the moving objects typically interact with the scene. We also want to develop approaches to improve the automatic classification of events and objects, partly using the user's feedback in order to more accurately identified the most relevant events and therefore take the most appropriate action upon detection of such event.