Fast head-shoulder proposal for deformable part model based pedestrian detection
TianRui Liu, Tania Stathaki · 2016
In this paper we propose a fast head-shoulder detector as a means to facilitating faster pedestrian detection. The proposed approach is based on the observation that human head-shoulder regions share relatively robust features. The purpose is to address the problem of high computational speed of the deformable part model (DPM) detector by selecting candidate regions with higher likelihood to contain pedestrians. The proposed head-shoulder detector is based on the simple, yet effective normed gradient features. Head-shoulder detector outputs regions which are strong candidates for the presence of pedestrians and therefore, pedestrian detection processes are performed only within these regions, avoiding exhaustive sliding window search across the entire test image. Additionally, a two-pedestrian detector is applied to reinforce the detection accuracy especially in scenarios where pedestrians are close to each other. Our experiments on the INRIA dataset indicate that the proposed pedestrian detection method achieves comparable detection rate to the DPM detector, with improved speed of implementation.