A Dynamic Measurement System Based on Adaptive Clustering and Multi-Classifier

This technology is highly suitable for detecting and tracking multi-skeletal targets in the field of aerial refueling. The paper presents a target feature responder based on K-means clustering, used for categorizing samples and training models. It employs a decision function to optimize the localization region across various classifier detection and tracking algorithms for targets. Additionally, a state judgment module scores the classifiers based on target state and depth information, which allows for adaptive selection of the classifier. To address the issue of missing target information, the method incorporates a stereo-vision-based mechanism to complete the localization region. This approach effectively handles challenges related to target appearance deformation, significant scale variations, motion blur, and occlusions.