  Pedestrian detection relying on deep convolution neural networks has made
significant progress. Though promising results have been achieved on standard
pedestrians, the performance on heavily occluded pedestrians remains far from
satisfactory. The main culprits are intra-class occlusions involving other
pedestrians and inter-class occlusions caused by other objects, such as cars
and bicycles. These result in a multitude of occlusion patterns. We propose an
approach for occluded pedestrian detection with the following contributions.
First, we introduce a novel mask-guided attention network that fits naturally
into popular pedestrian detection pipelines. Our attention network emphasizes
on visible pedestrian regions while suppressing the occluded ones by modulating
full body features. Second, we empirically demonstrate that coarse-level
segmentation annotations provide reasonable approximation to their dense
pixel-wise counterparts. Experiments are performed on CityPersons and Caltech
datasets. Our approach sets a new state-of-the-art on both datasets. Our
approach obtains an absolute gain of 9.5% in log-average miss rate, compared to
the best reported results on the heavily occluded (HO) pedestrian set of
CityPersons test set. Further, on the HO pedestrian set of Caltech dataset, our
method achieves an absolute gain of 5.0% in log-average miss rate, compared to
the best reported results. Code and models are available at:
https://github.com/Leotju/MGAN.
