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18 March 2022 Target tracking: method and comparison
Jianzhe Li, Hang Yu
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Proceedings Volume 12168, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021); 121682N (2022) https://doi.org/10.1117/12.2631459
Event: International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021), 2021, Harbin, China
Abstract
Target tracking technology is an important research direction in computer vision, which has a wide range of applications, such as video surveillance, human-computer interaction, unmanned driving and so on. At present, target tracking technology has made great progress, especially the target tracking method using deep learning has achieved satisfactory results in the last two years, which has made a breakthrough in target tracking technology. This paper discusses the target tracking system from the two frameworks of correlation filter and Siamese network, and further examines the new target tracking method proposed. In the correlation filter, we analyze the improved algorithms such as CACF, ECO, MOSSE and SRDCF, However, all the above methods based on correlation filtering are affected by boundary effect. In order to overcome this problem, SRDCF came into being. SRDCF uses spatial regularization to punish the correlation filter coefficients, and obtains results comparable to deep learning tracking methods. In Siamese network, we analyze the algorithm frameworks of SiamR-CNN, SiamMask, SiamRN, SiamRPN++, SiamFC. In this paper, we review the object tracking task and split it into two main aspects: CF, Siamese network. For each framework, we survey the work and draw some opinions. Finally, we hope to help sort out ideas and help beginners get started quickly.
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Jianzhe Li and Hang Yu "Target tracking: method and comparison", Proc. SPIE 12168, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021), 121682N (18 March 2022); https://doi.org/10.1117/12.2631459
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KEYWORDS
Image filtering
Detection and tracking algorithms
Electronic filtering
Sensors
Optical tracking
Video
Convolution
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