Efficient and robust indoor people detection based on RGB-D camera
Qi He, Kuixiang Liu, Lei Qu · 2017
Despite great progress has been made in recent years, efficient and robust people detection continues to be a challenging problem in the filed of computer vision. In this paper, we propose a highly efficient indoor people detect method based on RGB-D sensor. First, two RGB and depth feature fusing strategies are proposed and compared. Secondly, an improved non-maximum suppression algorithm is proposed to further boost the performance of detection without increasing the time consumption. The detection speed of proposed approach can reach about 16 frame-per-second on Intel i7 CPU. Compared to the RGB based algorithm, our approach can decrease the average log undetected rate about 15.9%, and the accuracy can be improved by about 16%.