Human activity detection and recognition for video surveillance

We present a framework for detecting and recognizing human activities for outdoor video surveillance applications. Our research makes the following contributions: For activity detection and tracking, we improve robustness by providing intelligent control and fail-over mechanisms, built on top of low-level motion detection algorithms such as frame differencing and feature correlation. For activity recognition, we propose an efficient representation of human activities that enables recognition of different interaction patterns among a group of people based on simple statistics computed on the tracked trajectories, without building complicated Markov chain, hidden Markov models (HMM), or coupled hidden Markov models (CHMM). We demonstrate our techniques using real-world video data to automatically distinguish normal behaviors from suspicious ones in a parking lot setting, which can aid security surveillance