Correlation Filter Based on Low-rank and Context-aware for Visual Tracking

Discriminative correlation filter(DCF)-based visual tracking approaches have attracted remarkable attention due to their good tradeoff between accuracy and robustness while running at real-time.However,the existing trackers still face model drift and even tracking failure situation when there are interferences such as long-term occlusion,out-of-view and out-of-plane rotation.To this end,we propose a low-rank and context-aware correlation filter(LR_CACF).Specifically,we directly integrate the target and its global contexts into DCF framework during filter learning stage to better discriminate the target from surrounding.Meanwhile,the low-rank constraint is injected across frames to emphasize the temporal smoothness,so that the learned filter is retained in a low-dimensional discriminant manifold to further improve tracking performance.Then,the ADMM is used to optimize the model effectively.Moreover,for model distortion,the multimodal detection mechanism is utilized to identify anomaly in the response.The filter stops training while extends the search regions to recapture the target when feedback is unreliable.Finally,extensive experiments are conducted on OTB50,OTB100 and DTB70 datasets,and the results demonstrate that,compared with the baseline SAMF_CA,LR_CACF achieves gains of 6.9%,4.0% and 7.1% in DP,respectively,and the average AUC improves by 3.6%,2.7% and 5.4%,respectively.Meanwhile,attribute-based evaluation shows that the proposed tracker is parti-cularly adept at handling the scenes such as occlusion,out-of-view,out-of-plane rotation,low resolution,and fast motion.