Abstract
Aiming at fast moving targets, such as ships, high-speed vehicles and athletes, this paper discusses a series of target detection algorithms based on neural network, YOLOv3 and background modeling. Compared KCF tracking with SSD tracking, Gaussian filter was applied to remove noise from pictures, and edge preserving filter was used to preserve edge features. Moreover, the algorithm combining deepsort tracking algorithm with YOLOv3 detection algorithm can improve the accuracy of YOLOv3 target detection, solve the problem of target loss during target tracking, adjust the frame size in real time, and improve the fit with the target position. Experiments show that the proposed algorithm based on detection before tracking has strong learning ability and robustness to unknown environment.
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Wang, Y., Liang, Z., Cheng, X. (2021). Fast Target Tracking Based on Improved Deep Sort and YOLOv3 Fusion Algorithm. In: Zeng, J., Qin, P., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2021. Communications in Computer and Information Science, vol 1451. Springer, Singapore. https://doi.org/10.1007/978-981-16-5940-9_27
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DOI: https://doi.org/10.1007/978-981-16-5940-9_27
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