Reliably detecting humans in crowded and dynamic environments using RGB-D camera

Real-time human detection in crowded and dynamic environments poses a significant challenge, due to complex background, occlusion and different human poses. In this paper, we propose a two-staged approach using color and depth data taken by an RGB-D camera. The first stage is to find plausible head-top locations quickly in depth image. The second stage is to extract effective discrimination features from RGB-D data to discard the false positives with support vector machine. The experiments on the publicly available office dataset, mobile platform dataset and clothing store dataset show that the system achieves accurate and real-time detection results on a single CPU core and its performance is significantly superior to state-of-the-art methods.