Most existing methods for abnormal event detection in the literature are relied on a training phase. Different from conventional approaches for abnormal event detection, a saliency attention based abnormal event detection approach is proposed in this paper. It is inspired by the visual attention mechanism that abnormal events are those which attract attention mostly in videos. The temporal and spatial abnormal saliency maps are firstly constructed and then the final abnormal event map is formatted by fusing them using a method with dynamic coefficients. The temporal abnormal saliency map is constructed by motion contrast between keypoints extracted from two successive video frames. The spatial abnormal saliency map is structured based on the color contrasts. Experiments performed on the benchmark datasets show that the proposed method achieves a high accurate and robust results for abnormal event detection without a training phase.
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