A robust object tracking method for infrared target
Qiang Fan, Erbo Zou, Gangbo Sun · 2020
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. Here, we propose an end-to-end lightweight Siamese convolution neural network to achieve fast and robust target tracking especially for infrared target. The network structure replaces the hand-crafted features by the multi-layers deep convolution features of the target, so that higher precision can be achieved. Specifically, object location is updated in every frame by refreshing a response-map. However, the success rate of tracking task decreases dramatically when the background becomes complex or occluded. Consequently, a simple and robust anti-occlusion tracking method is presented. The tracking accuracy is evaluated during tracking process by computing the tracking confidence parameters. The parameters are composed of two parts: target confusion degree which indicates the degree of background interference and target occlusion degree which indicates the degree of target occlusion. 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.