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Spatial-temporal context-aware abnormal event detection based on incremental sparse combination learning

Real-time abnormal event detection in practical video surveillance has been a difficult task, because there are a huge amount of continuous arrival video data, where normal events may change and only a small portion of video data contains abnormal events. In this paper, to address this problem, we use the latter arrived data to online update our model in an incremental way. We propose a spatial-temporal context-aware abnormal event detection method based on incremental sparse combination learning (ISCL). To better represent an event, we propose a novel Gradient Local Binary Patterns on Orthogonal Planes (GLBPOP) which is extracted by combining 3D gradient information and spatial-temporal context information together on two orthogonal planes. Then we propose an ISCL framework to update each dictionary in combination set in an incremental way to adapt to the possible varied upcoming normal samples. Experiments on public dataset demonstrate the proposed method is effective and superior.