Combination of self-organization map and kernel mutual subspace method for video surveillance

Abstract

This paper addresses the video surveillance issue of automatically identifying moving vehicles and people from continuous observation of image sequences. With a single far-field surveillance camera, moving objects are first segmented by simple background subtraction. To reduce the redundancy and select the representative prototypes from input video streams, the self-organizing feature map (SOM) is applied for both training and testing sequences. The recognition scheme is designed based on the recently proposed kernel mutual subspace (KMS) model. As an alternative to some probability-based models, KMS does not make assumptions about the data sampling processing and offers an efficient and robust classifier. Experiments demonstrated a highly accurate recognition result, showing the model's applicability in real-world surveillance system.