Joint Representation Learning with Deep Quadruplet Network for Real-Time Visual Tracking

Recently, trackers based on Siamese networks have attracted spread attention in the field of tracking because of a balance between accuracy and speed. Learning powerful representation via effective offline training strategy is critical for constructing high performance Siamese trackers. However, features extracted in most networks cannot accurately distinguish a tracked target from the background with semantic information in some challenging scenes. In this paper, we develop a Fully-Convolutional deep Quadruple Network (QuadFC) to learn more expressive representation via a novel multi-task loss function composed of a differential pairwise loss for tracking and a constructed triplet loss for similarity learning, which can be trained offline in an end-to-end mode. During inference, the proposed deep architecture does not need to update model and the positive-negative branches are removed to avoid unnecessary calculations. In particular, our approach is able to extract more discriminative features and perform robust visual tracking, due to joint representation learning and taking full use of original samples via the combination of positive-negative pairs. Furthermore, theoretical analysis of QuadFC is carried out through comparing the gradients of different loss functions. Extensive experiments on several tracking benchmarks, show that the proposed tracker achieves the state-of-the-art tracking performance while running at 68 FPS. The code can be available at https://github.com/DavidZhangdw/QuadFC.