Multi-View People Detection in Large Scenes via Supervised View-Wise Contribution Weighting

• URL: http://arxiv.org/abs/2405.19943v1
• Date: Thu, 30 May 2024 11:03:27 GMT
• Title: Multi-View People Detection in Large Scenes via Supervised View-Wise Contribution Weighting
• Authors: Qi Zhang, Yunfei Gong, Daijie Chen, Antoni B. Chan, Hui Huang,
• Abstract summary: This paper focuses on improving multi-view people detection by developing a supervised view-wise contribution weighting approach. A large synthetic dataset is adopted to enhance the model's generalization ability. Experimental results demonstrate the effectiveness of our approach in achieving promising cross-scene multi-view people detection performance.
• Score: 44.48514301889318
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent deep learning-based multi-view people detection (MVD) methods have shown promising results on existing datasets. However, current methods are mainly trained and evaluated on small, single scenes with a limited number of multi-view frames and fixed camera views. As a result, these methods may not be practical for detecting people in larger, more complex scenes with severe occlusions and camera calibration errors. This paper focuses on improving multi-view people detection by developing a supervised view-wise contribution weighting approach that better fuses multi-camera information under large scenes. Besides, a large synthetic dataset is adopted to enhance the model's generalization ability and enable more practical evaluation and comparison. The model's performance on new testing scenes is further improved with a simple domain adaptation technique. Experimental results demonstrate the effectiveness of our approach in achieving promising cross-scene multi-view people detection performance. See code here: https://vcc.tech/research/2024/MVD.

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