Investigating resource efficient algorithms for monitoring in video surveillance

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This project aims at designing video analytic algorithms that will be implemented in a prototype smart camera.They are: 1) Detection of an unauthorized person/intrusion and 2) Detection of dwelling. Although, severalmethods exist in the scientific literature for performing such tasks, most of them are either not very robust ornot adapted for running on a embedded platform with more limited resources (processor and memory). Ourpartner company wishes to perform on an embedded system these tasks in real-time. Our investigations willtherefore focus on researching methods and algorithms that can be implemented efficiently in a smart camera,yet that are robust, for detecting intrusion and dwelling in surveillance videos.We will investigate how subsampling can be used to process fewer pixels per frame, but yet detect the objectsof interest in background subtraction algorithms. We will also investigate the background update mechanismsto select or adapt only the most useful ones. Furthermore, we will design a motion history approach toaccumulate the detected foreground detections. Decisions will be made based on the accumulation map usingmachine learning techniques.