Pedestrian detection and semantic segmentation are highly correlated tasks which can be jointly used for better performance. In this paper, we propose a pedestrian detection method making use of semantic labeling to improve pedestrian detection results. A deep learning based semantic segmentation method is used to pixel-wise label images into 11 common classes. Semantic segmentation results which encodes high-level image representation are used as additional feature channels to be integrated with the low-level HOG+LUV features. Some false positives, such as falsely detected pedestrians located on a tree, can be easier eliminated by making use of the semantic cues. Boosted forest is used for training the integrated feature channels in a cascaded manner for hard negatives mining. Experiments on the Caltech-USA pedestrian dataset show improvements on detection accuracy by using the additional semantic cues.
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