Tracking with Extraction of Moving Object under Moving Camera Environment

This paper proposes a new approach to archive the robust tracking of moving objects under moving camera environment where the similar moving objects cross each other. Tracking with moving camera sometimes fails to track the object with similar color objects or similar background. Proposed approach is a particle filter based approach. It introduces the likelihood calculated by probabilistic background model which is constructed using dense optical flow and fast density estimation. Proposed approach introduces SVM (Support Vector Machine)1 to judge the scene where it is difficult to construct the probabilistic background model with non-uniform optical flow. This SVM uses the degree histogram of optical flow. Usefulness of proposed approach is evaluated in the experiments using actual video and the performance is compared with recent tracking approaches by quantitative evaluations.