Key Parts Context and Scene Geometry in Human Head Detection

Abstract

Due to the relatively fixed shape and color, head detection is widely used in many computer vision tasks, such as finding people and crowd counting. In this paper, we propose a method to detect human heads including: (1) A pairwise CNN model based on key parts context of human head and shoulder. (2) A fusion algorithm by using the priority of scene geometry structure. (3) To further test our approach, we collect a dataset about the passengers inside the bus with head annotations. This dataset composed of 2316 representative images extracted from a total of twenty hours of video. We evaluate our method on two indoor human head datasets and achieve state-of-the-art performance.