Distribution fields visual tracking based on Newton style convergence descending

Abstract

Traditional mean shift method has the limitation that could not effectively represent object accurately and converge the correct target position fast. To address this problem, in this paper, we propose a novel tracking algorithm using a Newton style convergence descending way based on distribution Fields target representation scheme. In contrast with traditional mean shift algorithm, the computational efficiency is greatly improved due to the SSD form of the histogram in distribution Fields and the efficient Newton style search. Our method adds uncertainty information of the target model and ensures a more accurate convergence to the true target. Moreover, we use a Kalman filter to predict target location in modified mean shift framework. This contributes to shorten convergence speed of the algorithm. Experiment results on several challenging video sequences have verified that the proposed algorithm is efficient and effective in many complicated scenes.