Generating Reliable Online Adaptive Templates for Visual Tracking

Abstract

Online adaption of visual tracking is a significant strategy to achieve good tracking performance since the appearance of the object target varies all along with the sequence. However, directly using the tracking results of previous frames to update the model will cause drifting, resulting in tracking failure. We propose a task-guided generative adversarial network (GAN), named TGGAN, to learn the general appearance distribution that a target may undergo through a sequence. Then the online adaption is simply to select templates from the images that are generated from the ground truth template in the first frame and a set of random vectors by the generator. This strategy helps the model alleviate drifting while still obtaining adaptivity. Tracking is treated as a template matching problem under a proposed Siamese matching network structure. Experiments show the effectiveness of the proposed online adaption strategy and the Siamese matching network.