A robust anti-occlusion object tracking method

Abstract

Visual object tracking is one of the most attractive issue in computer vision. Recently, deep neural network has been widely developed in object tracking and showing great accuracy. In general, the accuracy of tracking task decreases dramatically when the background becomes complex or occluded. Thus, a robust tracking method based on convolutional neural network and anti-occlusion mechanic is presented. Benefit from the adaptive tracking confidence parameter T, the tracking effect is evaluated during tracking. Once the target is occluded, the location of the target object is corrected immediately. Experimental results demonstrate that the proposed framework achieves state-of-the-art performance on the popular OTB50 and OTB100 benchmarks.