MultiFeature Based Visual Saliency Detection in Surveillance Video The perception of video is different from that of image because of the motion information in video. Motion objects lead to the difference between two neighboring frames which is usually focused on. By far, most papers have contributed to image saliency but seldom to video saliency. Based on scene understanding, a new video saliency detection model with multifeatures is proposed in this paper. First, background is extracted based on binary tree searching, then main features in the foreground is analyzed using a multiscale perception model. The perception model integrates faces as a high level feature, as a supplement to other lowlevel features such as color, intensity and orientation. Motion saliency map is calculated using the statistic of the motion vector field. Finally, multifeature conspicuities are merged with different weights. Compared with the gaze map from subjective experiments, the output of the multifeature based video saliency detection model is close to gaze map.

Salient Object Detection based on Multiple Priors Fusion Salient object detection can be utilized to detect the most significant regions in various environments, which has been regarded as foundation of computer vision. Different saliency models use different prior or knowledge. We propose a multi priors fusion method for saliency measure, which integrates background prior with foreground prior and center prior. Firstly, through each boundary of the image, we can get four saliency maps, and fuse them to get the background prior saliency map. Secondly, we utilize boundary extension method to highlight regions, and these regions can be regarded as the queries of manifold ranking for the foreground prior saliency map. Thirdly, the corners on the image are obtained, filtered by the foreground region, and then clustered into a point as the center of Gaussian model, which is used to calculate the center prior saliency map. Finally, the above three kinds of priorbased saliency maps are fused via the proposed fusion framework to gain a better final saliency map. Compared with fifteen methods, the experimental results on ECSSD and MSRA10K show that our proposed method achieves better saliency detection results.