Filtered shallow-deep feature channels for pedestrian detection

The semantic segmentation task is highly related to detection and apparently can provide complementary information for detection. In this paper, we propose integrating deep semantic segmentation feature maps into the original pedestrian detection framework which combines feature channels with AdaBoost classifiers. Firstly, we develop shallow-deep channels by concatenating shallow hand-crafted and deep segmentation channels to capture appearance clues as well as semantic attributes. Then a set of manually designed filters are utilized on the new channels to generate more response feature maps. Finally a cascade AdaBoost classifier is learned for hard negatives selection and pedestrian detection. With abundant feature information, our proposed detector achieves superior results on Caltech USA 10x and ETH dataset.