3D information is valuable for the detection of humans in video streams

In this paper, we propose a technique based on 3D information (also called depth or range) for the detection of humans. First, a background subtraction technique operates to detect the silhouettes of humans and objects moving in the scene. Then, a machine learning algorithm is used to predict if a silhouette annotated with depth matches a human silhouette or not. The complete method is designed to cope with defects introduced during the segmentation step. Results, obtained on computer generated data, show that 3D depth data is a valuable information for detecting humans in that it improves over techniques based on binary silhouettes. In our experiments, we have reached an accuracy of 99.9% thanks to the depth information.