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Multi-scale Semantic Segmentation Enriched Features for Pedestrian Detection

Pedestrian detection, as a branch of computer vision, has many significant real world applications such as autonomous driving or human behavior analysis. In this paper, we propose a convolutional neural network (CNN) based pedestrian detection framework which can be trained end-to-end. We design a feature enrichment unit to produce more representative features to improve detection performance. The feature enrichment units receive feature maps from the body network layer by layer and convey features in a backward manner. Together they produce multi-scale semantic segmentation results as extra features and merge them with feature maps of the body network. Then the merged feature maps will be fed into the detector to produce final predictions. The feature enrichment unit is easy to embed into existing convolutional neural networks based detection frameworks since it receives and produces feature maps. We use an alternating training strategy to train the network for detection and segmentation respectively and achieve considerable accuracy. The multi-scale feature enrichment units improve detection accuracy significantly as proven by the experiments.