Abstract
In order to accurately collect images of moving targets and improve the accuracy of target tracking and detection, this paper proposes a new gray-scale image moving target stability tracking and detection method based on computer vision technology. Set camera parameters and light source parameters, and accurately collect moving target images through computer vision technology to improve the accuracy of moving target tracking and detection. The video image is preprocessed, and the specific preprocessing steps include image enhancement and edge detection. The random forest is used as the classifier to eliminate the background, generate a rough target ROI map, and implement the corresponding scale recognition on the ROI area to realize the recognition of moving objects in video images. Combining the Camshift algorithm and the Kalman filter algorithm, the existing moving target tracking method is improved, and the stable tracking of the moving target is implemented. The stability detection of moving targets is implemented by the background difference method, and the selected background difference method is the ViBe algorithm. The test results show that the design method correctly handles more frames and has a higher accuracy rate. It processes more frames per second and has lower tracking and detection errors. The average tracking detection time of this method is less than 3000 s.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Yoo, K., Chun, J.: Analysis of optimal range sensor placement for tracking a moving target. IEEE Commun. Lett. 24, 1700–1704 (2020)
Andriyanov, N.A.: Application of computer vision systems for monitoring the condition of drivers based on facial image analysis. Pattern Recogn. Image Anal. Adv. Math. Theory Appl. USSR 31(3), 489–495 (2021)
Xu, S., Wang, J., Shou, W., et al.: Computer vision techniques in construction: a critical review. Arch. Comput. Methods Eng. State Art Rev. 28(5), 3383–3397 (2021)
He, Z., Li, H., Wang, Z., et al.: Adaptive compression for online computer vision: an edge reinforcement learning approach. ACM Trans. Multimedia Comput. Commun. Appl. 17(4), 118.1–118.23 (2021)
Xu, X., Yuan, Z., Wang, Y.: Multi-target tracking and detection based on hybrid filter algorithm. IEEE Access 8, 209528 (2020)
Zheng, Y., Li, Q., Wang, C., et al.: Magnetic-based positioning system for moving target with feature vector. IEEE Access 8, 105472–105483 (2020)
Wang, W., Pei, Y., Wang, S.H., et al.: PSTCNN: explainable COVID-19 diagnosis using PSO-guided self-tuning CNN. Biocell 47(2), 373–384 (2023)
Wang, W., Zhang, X., Wang, S.H., et al.: Covid-19 diagnosis by WE-SAJ. Syst. Sci. Control Eng. 10(1), 325–335 (2022)
Xiong, Y., Liu, Y., Cheng, W., et al.: Research on vision system of grasshopper bionic robot. Comput. Simul. 38(12), 345–348, 361 (2021)
Cao, J., Sun, Y., Zhang, G., et al.: Target tracking control of underactuated autonomous underwater vehicle based on adaptive nonsingular terminal sliding mode control. Int. J. Adv. Rob. Syst. 17(2), 451–468 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Wang, N., Liu, Q. (2024). Stability Tracking Detection of Moving Objects in Video Images Based on Computer Vision Technology. In: Wang, B., Hu, Z., Jiang, X., Zhang, YD. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 533. Springer, Cham. https://doi.org/10.1007/978-3-031-50574-4_5
Download citation
DOI: https://doi.org/10.1007/978-3-031-50574-4_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-50573-7
Online ISBN: 978-3-031-50574-4
eBook Packages: Computer ScienceComputer Science (R0)