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Moving object detection is a key to intelligent video analysis. On the onehand, what moves is not only interesting objects but also noise and clutteredbackground. On the other hand, moving objects without rich texture are pronenot to be detected. So there are undesirable false alarms and missed alarms inmany algorithms of moving object detection. To reduce the false alarms andmissed alarms, in this paper, we propose to incorporate a saliency map into anincremental subspace analysis framework where the saliency map makes estimatedbackground has less chance than foreground (i.e., moving objects) to containsalient objects. The proposed objective function systematically takes accountinto the properties of sparsity, low-rank, connectivity, and saliency. Analternative minimization algorithm is proposed to seek the optimal solutions.Experimental results on the Perception Test Images Sequences demonstrate thatthe proposed method is effective in reducing false alarms and missed alarms.

Moving Object Detection in Video Using Saliency Map and Subspace Learning