Multi-View People Detection in Large Scenes via Supervised View-Wise Contribution Weighting
<|reference_start|>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. See code here: https://vcc.tech/research/2024/MVD.<|reference_end|>
@article{zhang2024multi-view,
    title={Multi-View People Detection in Large Scenes via Supervised View-Wise
  Contribution Weighting},
    author={Qi Zhang, Yunfei Gong, Daijie Chen, Antoni B. Chan, Hui Huang},
    journal={arXiv preprint arXiv:2405.19943},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.19943},
    primaryClass={cs.CV}
}