Crowd recognition based on the fusion of multi-model collaboration

Guanghao Jin; Yuqing Wang; Jieying Wang; Junhua Zhao; Hui Du; Qingzeng Song

doi:10.1117/12.3029828

21 June 2024 Crowd recognition based on the fusion of multi-model collaboration

Guanghao Jin, Yuqing Wang, Jieying Wang, Junhua Zhao, Hui Du, Qingzeng Song

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 1316732 (2024) https://doi.org/10.1117/12.3029828
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

Crowd recognition by the deep learning is important to the management of campus or classroom, which include the technologies of object detection and face detection. In the crowd recognition case, the detection of anomaly samples plays key role like the person with no face or the opposite case. This study focuses on the cooperation of object detection model YOLOv8 and the advanced facial detection model SCNet, which is to find the anomaly samples. We try to provide a valuable insight into the research of object detection and facial recognition tasks in the field of computer vision, which is to provide useful guidance for further promoting the improvement of crowd recognition.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Guanghao Jin, Yuqing Wang, Jieying Wang, Junhua Zhao, Hui Du, and Qingzeng Song "Crowd recognition based on the fusion of multi-model collaboration", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 1316732 (21 June 2024); https://doi.org/10.1117/12.3029828

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