Noise-Suppressing Deep Tracking

In visual tracking, it is challenging to distinguish the target from similar objects called noises in the background. As deep trackers use convolutional neural networks for image classification as feature extractors, the extracted features are insensitive to different instances in the same class, which is prone to make prediction models confuse the target and the similar noises in the background. To this end, we propose a noise-suppressing algorithm to learn the discriminative representation for distinguishing the target from the noises in the background. First, we learn polynomial kernels for a search patch under the semantic guidance to increase the difference between representations of the target and the noises in the background. Second, we formulate the online foreground-background functions for the target and the noises in the background to learn an adaptive kernel, which suppresses the features positive for the noises and promotes the features positive for the target. We evaluate the proposed method on seven public datasets including OTB-2013, OTB-2015, VOT-2018, LaSOT, TrackingNet, GOT10k, and NFS. The comprehensive experimental results show that the proposed algorithm performs favorably against state-of-the-art methods, while running at real-time speed.