Enhancing Siamese Network by Color Histogram Based Bayes Classifier for Distractor-aware Object Tracking

Recently, Siamese networks have drawn great attention in visual tracking community because of their balanced accuracy and speed. By comparing the target patch with the candidate windows in a search region, we can track the object to the location where the highest similarity score is obtained. However, in Siamese Network, pairs of training data come from different frames of the same video, and for each search area, the non-semantic background occupies the majority, while semantic entities and distractor occupy less. This imbalanced distribution makes the training model hard to learn instance-level representation, but tending to learn the differences between foreground and background. For the targets those with large differences in the background also achieve high scores, and even some extraneous objects get high scores. To overcome this limitation, we enhance Siamese Network by color histogram based Bayes classifier. This method allows us to identify potentially distracting regions in advance. The risk of drifting is significantly reduced. Experiment results show that our tracker achieves state-of-the-art performance.