Generating Novel Information Salient Maps for Foreground Object Detection in Video
The conceptual model of visual saliency in human vision system has been employed in extracting salient features from images and multimedia data in the last decade. This paper proposes to employ the visual saliency for moving object detection. The crucial factor is to compute a saliency map such that visual attention can be performed. This paper proposes a new method for saliency map construction based on information theory and spatio-temporal model, called information saliency map (ISM). The ISM provides rich information content of the video. Moving object detection are then performed based on the ISM. Two popular and publicly available visual surveillance databases from CAVIAR and PETS are selected for evaluation. Experimental results show that the proposed method is robust for moving object detection in complex background and illumination changes. The average detection rate is 90.35% while the false alarm rate is 2.46% in CAVIAR (INRIA entrance hall) dataset with ground truth data, and it has shown merits comparing with the current state of the art.