Adaptive Multi-feature Fusion for Correlation Filter Tracking

Abstract

Robust visual object tracking is a challenging task in computer vision. Recently correlation filter-based trackers (CFTs) have aroused increasing interests because of the good performance and high efficiency. However, most feature representations for CFTs are not discriminative enough, which makes the trackers unreliable in complicated and changing scenarios. To address the problem, this paper presents an adaptive multi-feature fusion method based on kernelized correlation filter (KCF) framework. First we select HOG, LBP and grayscale feature for fusion to obtain more complementary and powerful feature. Then we propose a novel multi-feature fusion strategy, and adaptively calculate the feature’s fusion weight using probability separability criterion. The experimental results show that our method not only achieves better accuracy compared with existing features for KCF tracker, but also achieves state-of-the-art performance when running at 87 frames per second.