earticle

영어

In the unconstrained environment for video tracking is essential for many applications, such as video surveillance, man-machine interaction. In fact, moving object in the sequences generally has the context information of others or the different moments of its own state. Our research focus on the complex scenes, tracking multiple articulated targets, obtaining the features of the target, getting the precise target segmentation and improving the accuracy and reliability of tracking. We propose using top-down segmentation to feedback object detection, also contains the shape information. And the local appearance information is embedded into the framework of the level set. Then we propose a method to solve the interference of similar appearance target and multi-target tracking, by using context information to create two auxiliary items: Misleading items and support items. Both of them are using continuous random ferns. We experimentally evaluate our proposed approach on challenging sequences and video in real-world demonstrate its good performance in practice.