Real-time Human Detection based on Personness Estimation

In this work, we study a real-time human detection method for mobile devices using window proposals. We find that the normed gradients, designed for generic objectness estimation, are also able to rapidly generate high quality object windows for a singlecategory object. We also notice that fusing the normed gradients with additional color feature improves the performance of objectness estimation for the single-category object. Based on these observations, we propose an efficient method, which we call personness estimation, to produce candidate windows that are highly likely to contain a person. The produced candidate windows are used to search over feature maps of an image so that a human detection method can achieve high detection performance within a short period of time. We further present how personness estimation can be efficiently combined into part-based human detection. Our experiments indicate that the proposed method is directly applicable to mobile devices, and allows real-time human detection.