Covariance intersection fusion for visual tracking with hierarchical features

Abstract

In recent years, several visual tracking methods have applied multilayer convolutional features to correlation filters, but they mostly use fixed weights to fuse the multilayer response maps, which is difficult to adapt to various scene changes. To address this problem, a robust tracking algorithm based on adaptive fusion of multilayer response maps is proposed. In this paper, we extract multilayer convolutional features from the target's candidate area to improve the tracking robustness and the translation correlation filter is feed with CNN features extracted from each layer. Different from previous methods, we proposed a fast covariance intersection algorithm to adaptive fuse the multilayer response maps. After the final target center position is determined, we adopted a 1D scale filter through multi-scale sampling with HOG features to handle large scale variations. Moreover, in order to solve the problem of tracking drifts due to the severe occlusion and error accumulation, we present a new random update mechanism to update the translation filters. The experimental results on some challenging benchmark datasets show that the proposed algorithm achieves the outstanding performance against the state-of-the-art tracking methods.