Incremental Learning With Saliency Map for Moving Object Detection

Moving object detection is a key to intelligent video analysis. On the one hand, what moves are not only interesting objects but also noise and cluttered background. On the other hand, moving objects without rich texture are prone to not be detected. Therefore, there are undesirable false alarms and missed alarms in the results of many algorithms of moving object detection. To reduce the false alarms and missed alarms, in this paper we propose to incorporate a saliency map into an incremental subspace analysis framework in which the saliency map makes the estimated background have less of a chance than the foreground (i.e., moving objects) to contain salient objects. The proposed objective function systematically takes into account the properties of sparsity, low rank, connectivity, and saliency. An alternative minimization algorithm is proposed to seek the optimal solutions. The experimental results on both the Perception Test Images Sequences data set and Wallflower data set demonstrate that the proposed method is effective in reducing false alarms and missed alarms.