Reliable Memory Model for Visual Tracking

Effectively learning the appearance change of a target is the key point of an online tracker. When occlusion and misalignment occur, the tracking results usually contain a great amount of background information, which heavily affects the ability of a tracker to distinguish between targets and backgr...

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Veröffentlicht in:Electronics (Basel) 2021-10, Vol.10 (20), p.2488, Article 2488
Hauptverfasser: Ge, Daohui, Liu, Ruyi, Li, Yunan, Miao, Qiguang
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Sprache:eng
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Zusammenfassung:Effectively learning the appearance change of a target is the key point of an online tracker. When occlusion and misalignment occur, the tracking results usually contain a great amount of background information, which heavily affects the ability of a tracker to distinguish between targets and backgrounds, eventually leading to tracking failure. To solve this problem, we propose a simple and robust reliable memory model. In particular, an adaptive evaluation strategy (AES) is proposed to assess the reliability of tracking results. AES combines the confidence of the tracker predictions and the similarity distance, which is between the current predicted result and the existing tracking results. Based on the reliable results of AES selection, we designed an active-frozen memory model to store reliable results. Training samples stored in active memory are used to update the tracker, while frozen memory temporarily stores inactive samples. The active-frozen memory model maintains the diversity of samples while satisfying the limitation of storage. We performed comprehensive experiments on five benchmarks: OTB-2013, OTB-2015, UAV123, Temple-color-128, and VOT2016. The experimental results show that our tracker achieves state-of-the-art performance.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics10202488