TY - GEN
T1 - Local Adaptive Clustering Based Image Matching for Automatic Visual Identification
AU - Zhang, Yang
AU - Wang, Zhizhen
AU - Xu, Yuan
AU - He, Yanlin
AU - Zhu, Qunxiong
AU - Sheng, Hao
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In industrial production, monitoring cameras are widely used to pay attention to the running status of equipments. With the development of computer vision, it is possible to use image features to recognize devices. In this paper, a vision-aided identification system is designed to realize real-time automatic labeling of equipments by image matching in surveillance videos. The ORB algorithm is used to extract the image features, and the GMS algorithm is used to eliminate the wrong matching points. Then, according to the clustering locality and template locality, a Local Adaptive Clustering (LAC) method is proposed to optimize the position of labels. Matching templates are segmented with the center of clusters, which improves the efficiency and stability of labels. Experimental results show that LAC significantly suppresses the drift of labels.
AB - In industrial production, monitoring cameras are widely used to pay attention to the running status of equipments. With the development of computer vision, it is possible to use image features to recognize devices. In this paper, a vision-aided identification system is designed to realize real-time automatic labeling of equipments by image matching in surveillance videos. The ORB algorithm is used to extract the image features, and the GMS algorithm is used to eliminate the wrong matching points. Then, according to the clustering locality and template locality, a Local Adaptive Clustering (LAC) method is proposed to optimize the position of labels. Matching templates are segmented with the center of clusters, which improves the efficiency and stability of labels. Experimental results show that LAC significantly suppresses the drift of labels.
KW - Automatic Identification
KW - Image Matching
KW - Local Clustering
UR - https://www.scopus.com/pages/publications/85189297634
U2 - 10.1109/CAC59555.2023.10450332
DO - 10.1109/CAC59555.2023.10450332
M3 - 会议稿件
AN - SCOPUS:85189297634
T3 - Proceedings - 2023 China Automation Congress, CAC 2023
SP - 5686
EP - 5690
BT - Proceedings - 2023 China Automation Congress, CAC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 China Automation Congress, CAC 2023
Y2 - 17 November 2023 through 19 November 2023
ER -