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Deep Correlation Filter Tracking With Shepherded Instance-Aware Proposals
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DOI：10.1109/TITS.2021.3103601.png)
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Abstract
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Visual tracking is a core component of intelligent transportation systems and it is crucial to reduce or avoid traffic accidents. Recently, deep correlation filter (DCF) based trackers have exhibited good tracking performance. However, existing DCF based trackers are still ineffective to cope with large scale variations and severe distortions (e.g., heavy occlusions, significant deformations, large rotations, etc.), leading to the inferior performance. To address these issues, we develop a novel DeepCFIAP++ tracker, which incorporates effective shepherded instance-aware proposals into DCFs. DeepCFIAP++ can not only estimate the target scale at every frame but also re-detect the target in the case of severe distortions. Firstly, we propose to exploit both color and edge cues to generate complementary detection proposals to effectively handle various challenging scenarios. Then, we propose to utilize multi-layer target-specific deep features to rank the generated detection proposals and choose the instance-aware proposals, which will result in more robust tracking performance. Finally, we propose to use the DCFs to shepherd the instance-aware proposals toward their best locations, which will result in more accurate tracking results. Experimental results on five challenging datasets (i.e., OTB2013, OTB2015, VOT2016, VOT2017 and UAV20L) demonstrate that DeepCFIAP++ performs competitively with several other state-of-the-art DCF based trackers.
Keywords:
Target tracking
Proposals
Correlation
Feature extraction
Image edge detection
Distortion
Task analysis
Deep correlation filters
color and edge cues
multi-layer target-specific deep features
shepherded instance-aware proposals
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