HeadNet: Pedestrian Head Detection Utilizing Body in Context

Gang Chen, Xufen Cai, Hu Han, Shiguang Shan, Xilin Chen

Abstract: Pedestrian head with arbitrary poses and size is prohibitively difficult to detect in many real world applications. An appealing alternative is to utilize object detection technologies, which tend to be more and more mature and faster. However, general object detection technologies can hardly work in complicated scenarios where many heads are often too small to detect. In this paper, we present a novel approach that learns a semantic connection between pedestrian head and other body parts for head detection. Specifically, the proposed model, named as HeadNet, is based on PVANet backbone and also introduces beneficial strategies including online hard example mining (OHEM), fine-grained feature maps, RoI Align and Body in Context (BiC). Experiments demonstrate that our approach is able to utilize spatial semantics of the entire body effectively, and gains inspiring performance for pedestrian head detection.