Spatiotemporal Saliency Detection Based on Maximum Consistency Superpixels Merging for Video Analysis

Motion objects detection becomes more and more important in the applications of video surveillance, e.g., intrusion detection. The spatiotemporal saliency is an effective feature to describe object motion. However, there is a lot of redundancy in the spatial information preventing to obtain accurate saliency in an effective way. At the same time, temporal information cannot be accurately described because it is affected by uneven brightness, complex background, and fast-moving objects, especially at the edge of moving objects. In this article, we develop a novel method to tackle these problems and obtain more accurate spatiotemporal saliency. The key idea is the superpixel merging based on our maximum consistency model in feature space, through which the redundant spatial information is decreased and inhibit some temporal information errors. Experimental evaluations on the NNT dataset and surveillance videos show that the proposed method achieves better performance by comparing with some state-of-the-art methods, and can effectively detect intrusion entities.