Robust Visual Tracking Using Multiple Detectors by Trajectory Entropy Minimization

Abstract

We propose a tracking method based on minimization of multi-detector trajectory entropy. Multiple detectors are constructed online to track objects synchronously by mutual information-based feature selection. Each detector obtains its own tracking trajectory through continuous detection. The tracking results of all detectors are combined based on the minimization of trajectory entropy, and the optimal detector is selected from it to determine the target trajectory and achieve the target tracking task. Experimental results show that our tracker can handle more complex tracking environments and outperform many other state-of-the-art methods in terms of both success rate and precision.