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: this https URL

该论文通过开发一种监督式逐视图贡献加权方法改进多视角人员检测，并采用大型合成数据集增强模型的泛化能力，进而通过简单的领域自适应技术进一步提升模型在新的测试场景中的性能，实验证明该方法在跨场景多视角人员检测方面取得了有希望的成果。