A ROBUST IMAGE DESCRIPTOR FOR HUMAN DETECTION BASED ON HOG AND WEBER'S LAW

摘要

Human detection is essential for many applications such as surveillance and smart car. However, detecting humans in images or videos is a challenging task because of the variable appearance and background clutter. These factors affect significantly human shape. In this article, a robust descriptor based on HoG and Weber's Law is proposed. Namely, the proposed descriptor is concatenated by U-HoG and histogram of Weber's constant. Weber's constant has advantages such as robust to noise and detecting edge well. Because there are a large number of weak edges in the cluttered background affecting the detection result, the proposed method uses Weber's constant to take off the weak edges. If a pixel is on the weak edge, the proposed method will ignore the pixel when computing the feature. Therefore, the proposed descriptor may inherit the advantages of Weber's Law. From the simulation results of human detection, it is noted that the key contribution of the proposed method is that it has better performance than other comparative methods and is more robust to Gaussian white noise than U-HoG.