  State-of-the-art pedestrian detectors have achieved significant progress on
non-occluded pedestrians, yet they are still struggling under heavy occlusions.
The recent occlusion handling strategy of popular two-stage approaches is to
build a two-branch architecture with the help of additional visible body
annotations. Nonetheless, these methods still have some weaknesses. Either the
two branches are trained independently with only score-level fusion, which
cannot guarantee the detectors to learn robust enough pedestrian features. Or
the attention mechanisms are exploited to only emphasize on the visible body
features. However, the visible body features of heavily occluded pedestrians
are concentrated on a relatively small area, which will easily cause missing
detections. To address the above issues, we propose in this paper a novel
Mutual-Supervised Feature Modulation (MSFM) network, to better handle occluded
pedestrian detection. The key MSFM module in our network calculates the
similarity loss of full body boxes and visible body boxes corresponding to the
same pedestrian so that the full-body detector could learn more complete and
robust pedestrian features with the assist of contextual features from the
occluding parts. To facilitate the MSFM module, we also propose a novel
two-branch architecture, consisting of a standard full body detection branch
and an extra visible body classification branch. These two branches are trained
in a mutual-supervised way with full body annotations and visible body
annotations, respectively. To verify the effectiveness of our proposed method,
extensive experiments are conducted on two challenging pedestrian datasets:
Caltech and CityPersons, and our approach achieves superior performance
compared to other state-of-the-art methods on both datasets, especially in
heavy occlusion case.
