Abnormal Event Analysis Using Patching Matching and Concentric Features

This paper proposes a novel patch-based approach for abnormal event detection from a mobile camera using concentric features. It is very different from traditional methods which require the cameras being static for well foreground object detection. Two stages are included in this system i.e., training and detection, for scene representation and exceptional change detection of important objects like paintings or antiques. Firstly, at the training stage, a novel scene representation scheme is proposed for large-scale surveillance using a set of corners and key frames. Then, at the detection stage, a novel patch matching scheme is proposed for efficient scene searching and comparison. The scheme reduces the time complexity of matching not only from search space but also feature dimension in similarity matching. Thus, desired scenes can be obtained extremely fast. After that, a spider-web structure is proposed for missing object detection even though there are large camera movements between any two adjacent frames. Experimental results prove that our proposed system is efficient, robust, and superior in missing object detection and abnormal event analysis.