Correlation Filter Tracking via Distractor-Aware Learning and Multi-Anchor Detection
| Autor: | Junhui Hou, Guochun Chen, Wenxiong Kang, Feiqi Deng, Gengzheng Pan, Yongxin Zhou |
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| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry Process (computing) Boundary (topology) Context (language use) 02 engineering and technology Tracking (particle physics) Filter (video) Video tracking 0202 electrical engineering electronic engineering information engineering Media Technology 020201 artificial intelligence & image processing Computer vision Artificial intelligence Electrical and Electronic Engineering business |
| Zdroj: | IEEE Transactions on Circuits and Systems for Video Technology. 30:4810-4822 |
| ISSN: | 1558-2205 1051-8215 |
| Popis: | Correlation filter has demonstrated the power in object tracking, benefiting from its superior speed and competitive performance. However, existing correlation filter based trackers (CFTs) are fragile for some inherent defects caused by the boundary effect. To address this issue, we propose a novel correlation filter based tracking framework by integrating three highly collaborative components, including a fast target proposal module, a distractor-aware filter, and a correlation filter based refiner. Specifically, the target proposal aims at determining some target-like regions in contexts efficiently, which provides target-like patches to learn a distractor-aware filter and detect. Multi-region strategy enlarges space fields for learning and prediction. The filter learned from both target and distractors enhances its ability to identify background. Therefore, our method is capable of evaluating multiple candidates in wider context with less risk of drifting to distractors, namely multi-anchor detection. Besides, the proposed Proposal-Detect-Refine hierarchical searching process progressively achieves data alignment between testing and training samples, which benefits for reliable model prediction. A refiner is used to fine-tune positions after multi-anchor detection for lessening error accumulation and preventing model from drifting. Comprehensive experiments on five challenging datasets, i.e. OTB2013, OTB2015, VOT2017, VOT19, and TC128, demonstrate that the proposed method achieves superior performance against the state-of-the-art methods. |
| Databáze: | OpenAIRE |
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