Discriminative correlation tracking based on spatial attention mechanism for low-resolution imaging systems
| Autor: | Xiaofeng Li, Xiaogang Yang, Naixin Qi, Yueping Huang, Ruitao Lu |
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| Rok vydání: | 2021 |
| Předmět: |
BitTorrent tracker
Computer science business.industry Mechanism (biology) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020207 software engineering 02 engineering and technology Tracking (particle physics) Computer Graphics and Computer-Aided Design Computer graphics Discriminative model Video tracking 0202 electrical engineering electronic engineering information engineering Key (cryptography) Benchmark (computing) 020201 artificial intelligence & image processing Computer vision Computer Vision and Pattern Recognition Artificial intelligence business Software |
| Zdroj: | The Visual Computer. 38:1495-1508 |
| ISSN: | 1432-2315 0178-2789 |
| DOI: | 10.1007/s00371-021-02083-9 |
| Popis: | Low-resolution images are characterized by blurring, less texture information, and lack of detail. Visual object tracking for low-resolution imaging systems remains a challenging task. In this paper, we propose a discriminative correlation tracking algorithm based on a spatial attention mechanism for low-resolution imaging systems (LSDCT) to address these challenges. The key innovations of our proposed algorithm include adjustable windows and a spatial attention mechanism. We design a generic adjustable window to mitigate boundary effects and employ the spatial attention mechanism to highlight the target in low-resolution images. We conduct qualitative and quantitative evaluations on three well-known benchmark datasets: OTB100, TC128, and UAV123. Extensive experimental results indicate that the proposed approach is superior to state-of-the-art trackers. |
| Databáze: | OpenAIRE |
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