Single Target Tracking Using Reliability Evaluation and Feature Selection

This paper proposes a visual tracking algorithm using reliability evaluation and feature selection mechanism in the framework of Correlation Filter (CF). Three additional modules are used to improve the current CF-based tracker. Firstly, a module of reliability evaluation is used to determine whether the current tracking result is reliable. Secondly, an updating module is used to determine whether to update the target model by comparing the reliability of current tracking result with historical average. Thirdly, a feature selection module is presented to select hand-crafted feature or deep convolutional feature according to the current tracking state. Experimental results on a benchmark dataset of fifty challenging test sequences show that the proposed method can reduce the interference of complex factors effectively.