A time-slice optimization based weak feature association algorithm for video condensation

There are a lot of complex environments in real scene, such as illumination variation, shadow variation, object occlusion, which will directly affect the performance of video synopsis. In this paper, we adopt Grid Background Model as object detection algorithm, proposing algorithm based on weak feature to solve the object occlusion problem, at last we propose to use time-slice optimization algorithm to solve the visualization problem of video condensation. Specifically, Grid Background Model is adopted to segment the foreground from the background, then we use current frame to update background frame, and then binarize the foreground frame to perform Neighborhood illumination invariant shadow elimination. A clear foreground can be obtained by doing the procedure above as well as Gaussian noise elimination and morphological operation such as inflation and corrosion to remove cavities. Meanwhile, the outline of the object is extracted by using the canny edge detector. In the object tracking section, we will introduce how to use the weak features, such as color, speed and direction on the basis of location prediction based on tracking algorithm to perform object association, and the extraction of accurate information of abstract and outline of the object at the same time. Finally, in the video condensation section, we will describe how to use optical time-slice based minimum energy model to perform video condensation according to frame sequence. The experimental result shows that, the method mentioned above can provide a new approach for solving the occlusion problems of video condensation, and have better visualization of abstract video, and achieve up to 6 times concentration to the original video.