1 research outputs found
Moving Object Detection in Video Using Saliency Map and Subspace Learning
Moving object detection is a key to intelligent video analysis. On the one
hand, what moves is not only interesting objects but also noise and cluttered
background. On the other hand, moving objects without rich texture are prone
not to be detected. So there are undesirable false alarms and missed alarms in
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 where the saliency map makes estimated
background has less chance than foreground (i.e., moving objects) to contain
salient objects. The proposed objective function systematically takes account
into the properties of sparsity, low-rank, connectivity, and saliency. An
alternative minimization algorithm is proposed to seek the optimal solutions.
Experimental results on the Perception Test Images Sequences demonstrate that
the proposed method is effective in reducing false alarms and missed alarms