Center contrastive loss regularized CNN for tracking
Ningning Li, Yun Zhou, Zhuqing Jiang, Xiaoqiang Guo · 2017
Visual tracking is a significant but challenging field in computer vision. Although considerable progress has been made in recent years, robust tracking in complicated scenes remains an open problem. Trackers get confused easily when similar objects appear or heavy clutter occurs due to indistinguishable features. In this work, a more effective feature extraction method based on convolutional neural network (CNN) is proposed. Different from conventional CNN models, this method applies a contrastive loss function to a single branch network, and centralization is employed to obtain more discriminative information. In addition, appropriate model update is used to capture transformation in object appearance. Quantitative experimental results on various video sequences demonstrate the superior performance of the proposed method in comparison with other state-of-the-art trackers. Complementary experiments are also conducted to validate some arguments.