SANet: Structure-Aware Network for Visual Tracking

Abstract

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, OTB100, TC-128 and VOT2015, show that the proposed algorithm outperforms other methods. Code is released at http://www.dabi.temple.edu/~hbling/code/SANet/SANet.html.

In CVPR Deep Vision Workshop, 2017

References in corpus (4)

• Sequence Transduction with Recurrent Neural Networks
• Siamese Instance Search for Tracking
• Multi-Dimensional Recurrent Neural Networks
• Multi-level Contextual RNNs with Attention Model for Scene Labeling

Cited by in corpus (6)

• Good Features to Correlate for Visual Tracking
• Siamese Cascaded Region Proposal Networks for Real-Time Visual Tracking
• Recurrent Filter Learning for Visual Tracking
• A Review of Visual Trackers and Analysis of its Application to Mobile Robot
• Locally Adaptive Learning Loss for Semantic Image Segmentation
• Scene Parsing via Dense Recurrent Neural Networks with Attentional Selection