An Adaptive Feature Channel Weighting Scheme for Correlation Tracking

Abstract

In most of Discriminative Correlation Filter (DCF) based trackers, they used a fixed weight for each feature channel for all incoming frames. However, in the experiment, we find that different features have pros and cons under different scenarios. In this paper, we propose to couple the response of a DCF based tracker with the weights of different feature channels to strengthen their positive effects while weaken their negative effects simultaneously. This coupling is achieved by an adaptive feature channel weighting scheme. The tracking is formulated as a two-stage optimization problem: the tracker is learned using the alternative direction method of multipliers (ADMM) and the weights of feature channels are adaptively adjusted by a least-square estimation. We integrate the adaptive feature channel weighting scheme into two state-of-the-art handcrafted DCF based trackers, and evaluate them on two benchmarks: OTB2013 and VOT2016, respectively. The experimental results demonstrate its accuracy and efficiency when compared with some state-of-the-art handcrafted DCF based trackers.