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| Main Author: | |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.01720 |
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| _version_ | 1866929196708659200 |
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| author | Wang, Zhizhen |
| author_facet | Wang, Zhizhen |
| contents | Monitoring cameras are extensively utilized in industrial production to monitor equipment running. With advancements in computer vision, device recognition using image features is viable. This paper presents a vision-assisted identification system that implements real-time automatic equipment labeling through image matching in surveillance videos. The system deploys the ORB algorithm to extract image features and the GMS algorithm to remove incorrect matching points. According to the principles of clustering and template locality, a method known as Local Adaptive Clustering (LAC) has been established to enhance label positioning. This method segments matching templates using the cluster center, which improves the efficiency and stability of labels. The experimental results demonstrate that LAC effectively curtails the label drift. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_01720 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Local Adaptive Clustering Based Image Matching for Automatic Visual Identification Wang, Zhizhen Computer Vision and Pattern Recognition Monitoring cameras are extensively utilized in industrial production to monitor equipment running. With advancements in computer vision, device recognition using image features is viable. This paper presents a vision-assisted identification system that implements real-time automatic equipment labeling through image matching in surveillance videos. The system deploys the ORB algorithm to extract image features and the GMS algorithm to remove incorrect matching points. According to the principles of clustering and template locality, a method known as Local Adaptive Clustering (LAC) has been established to enhance label positioning. This method segments matching templates using the cluster center, which improves the efficiency and stability of labels. The experimental results demonstrate that LAC effectively curtails the label drift. |
| title | Local Adaptive Clustering Based Image Matching for Automatic Visual Identification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.01720 |