Cited By
View all- Sun NZhao JShi QLiu CLiu P(2024)Moving Target Tracking by Unmanned Aerial Vehicle: A Survey and TaxonomyIEEE Transactions on Industrial Informatics10.1109/TII.2024.336308420:5(7056-7068)Online publication date: May-2024
Long Time Target Tracking Algorithm Based on Multi Feature Fusion and Correlation Filtering
Pages 29 - 35
Abstract
This paper considers the problem of long-term target tracking in complex scenes when tracking failures are unavoidable due to illumination change, target deformation, scale change, motion blur, and other factors. More specifically, we propose a target tracking algorithm, called Re-detection Multi-feature Fusion (RDMF), based on the fusion of Scale-adaptive kernel correlation filtering and re-detection. The target tracking algorithm trains three kernel correlation filters based on HOG, CN and LBP features, and then obtains the fusion weight of response graphs corresponding to different features based on APCE criterion, and uses weighted Average to complete the position estimation of the tracked target. In order to deal with the problem that the target is occluded and disappears in the tracking process, a random fern classifier is trained to perform re-detection when the target is occluded. After comparing the OTB-50 target tracking data set, the RDMF algorithm improves the range accuracy by 10.1% compared with SAMF algorithm, and is better than KCF, DSST, CN and other algorithms.
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September 2021
715 pages
ISBN:9781450384087
DOI:10.1145/3488933
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Publication History
Published: 25 February 2022
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- Research-article
- Research
- Refereed limited
Funding Sources
- Natural Science Foundation of Shaanxi Province
- Xi?an Science and Technology Projects
- International Cooperation and Exchange Program of Shaanxi Province
- National Natural Science Foundation of China
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AIPR 2021
AIPR 2021: 2021 4th International Conference on Artificial Intelligence and Pattern Recognition
September 24 - 26, 2021
Xiamen, China
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View all- Sun NZhao JShi QLiu CLiu P(2024)Moving Target Tracking by Unmanned Aerial Vehicle: A Survey and TaxonomyIEEE Transactions on Industrial Informatics10.1109/TII.2024.336308420:5(7056-7068)Online publication date: May-2024
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