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 address 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 to 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. Extensive experiments on three benchmarks, OT-B100, TC-128 and VOT2015, show that the proposed algorithm outperforms other methods. Code is released at
SANet REF incorporates recurrent neural network into CNNs to model the structure information within an object in addition to the traditional semantic information.
SANet: Structure-Aware Network for Visual Tracking
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Computer Science