Convolutional neural network (CNN) has drawn increasing interest in visual tracking owing to its powerfulness in feature extraction. Most existing CNN-based trackers treat tracking as a classification problem. However, these trackers are sensitive to similar distractors because their CNN models mainly focus on inter-class classification. To deal with this problem, we use self-structure information of object to distinguish it from distractors. Specifically, we utilize recurrent neural network (RNN) to model object structure, and incorporate it into CNN to improve its robustness in presence of similar distractors. Considering that convolutional layers in different levels characterize the object from different perspectives, we use multiple RNNs to model object structure in different levels respectively. In addition, we present a skip concatenation strategy to fuse CNN and RNN feature maps, and thus are able to provide the next layer with richer information, which further improves the performance of the proposed model. Extensive experimental results on three large-scale benchmarks, OTB100, TC-128 and VOT2015, show that the proposed algorithm outperforms other state-of-the-art methods.

使用递归神经网络结合卷积神经网络提取物体自身结构信息来增强视觉追踪系统对于相似干扰的鲁棒性。在 OTB100、TC-128 和 VOT2015 三个基准测试集上不仅表现优异，且代码已经公开发布。

SANet：面向结构感知的视觉跟踪网络