Visual Correlation Filter Tracking for UAV Based on Temporal and Spatial Regularization with Boolean Maps
Authors: 
Na Li, Jiale Gao, Ying Liu, Yansheng Zhu, Wenhan JiangAuthors Info & Claims
Na Li, Jiale Gao, Ying Liu, Yansheng Zhu, Wenhan JiangAuthors Info & ClaimsPages 372 - 377
Abstract
Object tracking is now widely used in sports event broadcasting, security surveillance, and human-computer interaction. It is a challenging task for tracking on unmanned aerial vehicle (UAV) datasets due to many factors such as illumination change, appearance modification, occlusion, motion blur and so on. To solve the problem, a visual correlation filter tracking algorithm based on temporal and spatial regularization is proposed. It employs boolean maps to obtain visual attention, and fuses different features such as color names (CN), histogram of oriented gradient (HOG) and Gray features to enhance the visual representation. New object occlusion judgment method and model update strategy are put forward to make the tracker more robust. The proposed algorithm is compared with other six trackers in terms of distant precision and success rate on UAV123. And the experimental results show that it achieves more stable and robust tracking performance.
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Index Terms
- Visual Correlation Filter Tracking for UAV Based on Temporal and Spatial Regularization with Boolean Maps
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September 2022
1221 pages
ISBN:9781450396899
DOI:10.1145/3573942
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Published: 16 May 2023
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AIPR 2022
AIPR 2022: 2022 5th International Conference on Artificial Intelligence and Pattern Recognition
September 23 - 25, 2022
Xiamen, China
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