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

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.

Authors

• Qi Zhang School of Computer Science, Fudan University, Shanghai, China Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China
• Yunfei Gong Shenzhen University
• Daijie Chen Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen University
• Antoni B. Chan Department of Computer Science, City University of Hong Kong, Hong Kong, China.
• Hui Huang Shenzhen University

Keywords

• CV: Motion & Tracking
• CV: Object Detection & Categorization
• CV: Scene Analysis & Understanding
• CV: Segmentation