A Novel Architecture of Pedestrian Detection
Wenshu Li, Menghui Ruan, Xiaoying Guo, Hongyan Wang, Hancui Hancui, Yang Han · 2019
In the past decade, the pedestrian detection has drawn much attention due to the significant role it plays in artificial intelligence system and vehicle assisted driving system. In order to achieve a balance between recognition rate and detection time, a novel architecture of pedestrian detection has been proposed in this paper. Firstly, we get the enhanced HOG feature (eHOG) by enhancing Histograms of Oriented Gradients feature contrast. Then, the eHOG is used as an input of XGBoost to recognize pedestrian/non-pedestrian. The architecture proposed is tested on MIT and INRIA pedestrian datasets, experimental results show that recognition rate can reach 91.13% on MIT dataset and 99.11% on INRIA dataset, and testify the effect of our architecture proposed.