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End-to-End Flow Correlation Tracking with Spatial-Temporal Attention
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Discriminative correlation filters (DCF) with deep convolutional features have achieved favorable performance in recent tracking benchmarks. However, most of existing D-CF 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 paper, we propose the FlowTrack, which focuses on making use of the
doi:10.1109/cvpr.2018.00064
dblp:conf/cvpr/ZhuWZY18
fatcat:rkxgrswoebbzzkhbvaiyfsiy34