End-to-end Flow Correlation Tracking with Spatial-temporal Attention
arXiv:1711.01124
Abstract
Discriminative correlation filters (DCF) with deep convolutional features have achieved favorable performance in recent tracking benchmarks. However, most of existing DCF trackers only consider appearance features of current frame, and hardly benefit from motion and inter-frame information. The lack of temporal information degrades the tracking performance during challenges such as partial occlusion and deformation. In this work, we focus on making use of the rich flow information in consecutive frames to improve the feature representation and the tracking accuracy. Firstly, individual components, including optical flow estimation, feature extraction, aggregation and correlation filter tracking are formulated as special layers in network. To the best of our knowledge, this is the first work to jointly train flow and tracking task in a deep learning framework. Then the historical feature maps at predefined intervals are warped and aggregated with current ones by the guiding of flow. For adaptive aggregation, we propose a novel spatial-temporal attention mechanism. Extensive experiments are performed on four challenging tracking datasets: OTB2013, OTB2015, VOT2015 and VOT2016, and the proposed method achieves superior results on these benchmarks.
arXiv commentAccepted in CVPR 2018
References in corpus (8)
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network
- DCFNet: Discriminant Correlation Filters Network for Visual Tracking
- Spatio-temporal video autoencoder with differentiable memory
- Flow-Guided Feature Aggregation for Video Object Detection
- Flowing ConvNets for Human Pose Estimation in Videos
- CREST: Convolutional Residual Learning for Visual Tracking
- Deep Motion Features for Visual Tracking