Two-level superpixel and feedback based visual object tracking

Abstract

While numerous superpixel-based tracking algorithms have been proposed and demonstrated successfully, there still remain some challenges, such as determining the number of superpixels, mining and exploiting the structural information of superpixels and handling the drifts. In this paper, we propose a tracking method with two-level superpixels and a novel update strategy based on feedback to deal with the challenges mentioned above. Firstly, Bilateral filter is introduced to filter out outliers and improve the boundary capability of object as well as segmentation of superpixels. Then two-level superpixel is proposed to determine superpixel number automatically through iterating instead of setting superpixel number empirically which affects the robustness of tracking algorithm. Moreover, a novel measuring method which considers color similarity and relative positions of superpixels is proposed to make a better use of structural information of superpixels and improve tracking performance by adding relative position of superpixels into the appearance model. Finally, a feedback based update strategy is presented to handle drifts existing in tracking by calculating the adaptation of appearance model and updating the parameters like superpixel number and relative position of superpixels. Experiments on challenging sequences and comparisons to state-of-the-art methods demonstrate the feasibility and effectiveness of the proposed tracking algorithm.