Moving Object Detection Algorithm Based on Pixel Background Sample Sets in Panoramic Scanning Mode

In order to overcome the excessive false detection of marginal noise and the object holes of the existing algorithm in outdoor panoramic surveillance, a moving object detection algorithm based on pixel background sample sets in panoramic scanning mode is proposed. In the light of the space distribution characteristics, neighborhood pixels have similar values. Therefore, a background sample set for each pixel is created by random sampling in the first scanning cycle which effectively avoids the false detection of marginal noise and reduces the time cost of background model establishment. The adjacent frame difference detection algorithm in the traditional camera motion mode is prone to object holes. To solve this problem, detection based on background sample sets is presented to obtain complete moving object region. The results indicate that the proposed moving object detection algorithm works more efficiently on reducing marginal noise interference, and obtains complete moving object information compared with the frame difference detection algorithm based on registration results in traditional camera motion mode, thereby meeting the needs of real-time detection as well as improving its accuracy.