Evaluation of sensors and algorithms for person detection for personal robots

The main objective of this project was to evaluate the sensing system for person detection in the scope of its integration in a mobile robotic platform for Ambient Assisted Living. Two sensors were considered, a camera and a depth sensor. For the camera (2D), 3 different algorithms were implemented: face detection, tracking and recognition. For the depth sensor the whole body detection was tested using skeleton tracking. The results showed that the face detection and recognition algorithms had a very small range, and the face tracking demonstrated to reach a higher distance. However, the latter exhibited poor results when confronted with illumination variations. The skeleton tracking algorithm provided good results, being capable of tracking relatively close to the sensor and up to 3 meters distance. Thus, the 2D face recognition can be used for short distances to identify the person, while the 3D skeleton tracking can be appropriated for distant tracking of the person. This work showed that the integration of these sensing systems, in a robotic platform, can make a robust robot capable of human interaction in home environments.