Paper
28 April 2023 Object tracking based on foreground adaptive bounding box and motion state redetection
Jingyi Fu, Qifeng Liang, Qingsong Xie, Zhiyong An
Author Affiliations +
Proceedings Volume 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022); 126102M (2023) https://doi.org/10.1117/12.2671281
Event: Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 2022, Wuhan, China
Abstract
Siamese network is successfully applied in object tracking. Most of the existing Siamese tracking methods extract template features in the first frame, which will cause the tracker to ignore the appearance change of the target in the subsequent video. In this paper, we propose a tracker based on foreground adaptive bounding box and motion state redetection. The tracker infers the reliability of tracking by the motion pattern of the bounding box. When an anomaly is detected, the tracker will redetect using the continuously updated template. Furthermore, our tracker employs an adaptive bounding box to avoid the effects of inaccurate rotation of the bounding box. The results on the VOT2018 dataset show that our tracker achieves stronger robustness and higher accuracy, providing superior performance compared to the current state-of-the-art trackers.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jingyi Fu, Qifeng Liang, Qingsong Xie, and Zhiyong An "Object tracking based on foreground adaptive bounding box and motion state redetection", Proc. SPIE 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 126102M (28 April 2023); https://doi.org/10.1117/12.2671281
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KEYWORDS
Reliability

Feature extraction

Video

Ablation

Deep learning

Distance measurement

Motion estimation

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