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arXiv · 2405.19943

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

Qi Zhang·Yunfei Gong·Daijie Chen·Antoni B. Chan·Hui Huang
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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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Qi Zhang, Yunfei Gong, Daijie Chen, Antoni B. Chan, Hui Huang. 2024-05-30. Multi-View People Detection in Large Scenes via Supervised View-Wise Contribution Weighting. https://arxiv.org/abs/2405.19943

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