Performance study of feature descriptors for human detection on depth map

Depth map contains the space information of objects and is almost free from the influence of light, and it attracts many research interests in the field of machine vision used for human detection. Therefore, hunting a suitable image feature for human detection on depth map is rather attractive. In this paper, we evaluate the performance of the typical features on depth map. A depth map dataset containing various indoor scenes with human is constructed by using Microsoft's Kinect camera as a quantitative benchmark for the study of methods of human detection on depth map. The depth map is smoothed with pixel filtering and context filtering so as to reduce particulate noise. Then, the performance of five image features and a new feature is studied and compared for human detection on the dataset through theoretic analysis and simulation experiments. Results show that the new feature outperforms other descriptors.