RGB-D human identification and tracking in a smart environment

Elderly and disabled people can particularly benefit from smart environments with integrated sensors, as they offer basic assistive functionalities enabling personal independence and increased safety. In a smart environment, the key issue is to quickly sense the location and identity of its users. In this paper, we aim at enhancing the robustness of human detection and identification algorithm in a home environment based on the Kinect, which is a new and multimodal sensor. The contribution of our work is that we employ different cameras for different algorithmic modules, based on investigating the suitability of each camera in Kinect for a specific processing task, resulting in an efficient and robust human detection, tracking and re-identification system. The total system consists of three processing modules: (1) object labeling and human detection based on depth data, (2) human reentry identification based on both RGB and depth information, and (3) human tracking based on RGB data. Experimental results show that each algorithmic module works well, and the complete system can accurately track up to three persons in a real situation.