CRACT: Cascaded Regression-Align-Classification for Robust Visual Tracking

• URL: http://arxiv.org/abs/2011.12483v1
• Date: Wed, 25 Nov 2020 02:18:33 GMT
• Title: CRACT: Cascaded Regression-Align-Classification for Robust Visual Tracking
• Authors: Heng Fan, Haibin Ling
• Abstract summary: We introduce an improved proposal refinement module, Cascaded Regression-Align- Classification (CRAC) CRAC yields new state-of-the-art performances on many benchmarks. In experiments on seven benchmarks including OTB-2015, UAV123, NfS, VOT-2018, TrackingNet, GOT-10k and LaSOT, our CRACT exhibits very promising results in comparison with state-of-the-art competitors.
• Score: 97.84109669027225
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High quality object proposals are crucial in visual tracking algorithms that utilize region proposal network (RPN). Refinement of these proposals, typically by box regression and classification in parallel, has been popularly adopted to boost tracking performance. However, it still meets problems when dealing with complex and dynamic background. Thus motivated, in this paper we introduce an improved proposal refinement module, Cascaded Regression-Align-Classification (CRAC), which yields new state-of-the-art performances on many benchmarks. First, having observed that the offsets from box regression can serve as guidance for proposal feature refinement, we design CRAC as a cascade of box regression, feature alignment and box classification. The key is to bridge box regression and classification via an alignment step, which leads to more accurate features for proposal classification with improved robustness. To address the variation in object appearance, we introduce an identification-discrimination component for box classification, which leverages offline reliable fine-grained template and online rich background information to distinguish the target from background. Moreover, we present pyramid RoIAlign that benefits CRAC by exploiting both the local and global cues of proposals. During inference, tracking proceeds by ranking all refined proposals and selecting the best one. In experiments on seven benchmarks including OTB-2015, UAV123, NfS, VOT-2018, TrackingNet, GOT-10k and LaSOT, our CRACT exhibits very promising results in comparison with state-of-the-art competitors and runs in real-time.

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