Tracking Using Superpixel Features
Jingjing Liu, Chen Ying, Cheng Zha, Yu Hua, Li Zhao · 2016
While great success has been demonstrated in numerous tracking algorithm, some challenging problems still remain such as motion, shape deformation and occlusion. In this paper, the proposed algorithm can work robustly to overcome the occlusion and fast movement in real -- world scenarios. A discriminative model based on the Gaussian superpixel model is constructed to descript the change of the target and the background. The calculation of the superpixel's weight uses the special information and a penalize factor. Furthermore, the tracking result is selected from the candidates generated under the framework of particle filter. The candidate with the highest score would be set to the tracking result. Besides, the update strategy which updates according to the trend of candidate's score is adaptive in order to suit for the change of the target. The experimental results demonstrate that the proposed algorithm performs more stable compared with several state-of-the-art algorithms when dealing with occlusion and fast movement.