  Pedestrian detection plays an important role in many applications such as
autonomous driving. We propose a method that explores semantic segmentation
results as self-attention cues to significantly improve the pedestrian
detection performance. Specifically, a multi-task network is designed to
jointly learn semantic segmentation and pedestrian detection from image
datasets with weak box-wise annotations. The semantic segmentation feature maps
are concatenated with corresponding convolution features maps to provide more
discriminative features for pedestrian detection and pedestrian classification.
By jointly learning segmentation and detection, our proposed pedestrian
self-attention mechanism can effectively identify pedestrian regions and
suppress backgrounds. In addition, we propose to incorporate semantic attention
information from multi-scale layers into deep convolution neural network to
boost pedestrian detection. Experiment results show that the proposed method
achieves the best detection performance with MR of 6.27% on Caltech dataset and
obtain competitive performance on CityPersons dataset while maintaining high
computational efficiency.
