Local Adaptive Clustering Based Image Matching for Automatic Visual
  Identification
Zhizhen Wang
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.