Exploiting Appearance Re-Emergence for Robust Visual Tracking

Le résumé fourni par la source

Performances of visual object trackers suffer greatly from appearance changes caused by shape deformation, illumination variation, camera motion and etc. Existing online updating approaches generally rely on fusing the initial template with successfully tracked samples to achieve adaptability and hence are sensitive to erroneous samples. Yet, directly modeling the complex appearance change is challenging, we observe that most object appearances assume temporal repetitiveness, which we can exploit for modeling the change in a data-driven manner. In the paper, we propose a reference template based tracking algorithm that exploits re-emergent samples for adapting the appearance variations. The proposed tracker maintains a bank of historically tracked templates with high confidence scores. From the bank, we then generate a multiple reference templates representation through clustering for both aggregating of future samples and searching for queried samples to produce better tracking results. We use cluster centers of the historical template bank as reference templates used for tracking, which automatically identifies re-emergent samples due to appearance similarity and resists the noise of individual templates in the bank meanwhile. Extensive experimental results on 6 challenging benchmarks including OTB2015, VOT2020, UAV123, LaSOT, GOT-10k, and TrackingNet show that our tracker outperforms all previous state-of-the-art methods, which demonstrates our historical template clustering strategy effectively adapts to the target appearance changes.

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