  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.
