A robust human head detection method for human tracking

• Published in: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems pp. 4558 - 4563
• Main Authors: Hosub Yoon, Dohyung Kim, Suyoung Chi, Youngjo Cho
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2006
• Subjects: Cameras; Head; human head tracking; Humans; Lighting; Object detection; Robot control; Robot vision systems; Robustness; Shape; Skin
• ISBN: 9781424402588; 1424402581
• ISSN: 2153-0858; 2153-0866
• DOI: 10.1109/IROS.2006.282159

In this paper, an algorithm for human head detection over a distance exceeding 2.5 m between a camera and an object is described. This algorithm is used for the control of a robot, which has the additional limits of a moving camera, moving objects, various face orientations, and unfixed illuminations. With these circumstances, only the assumption that human head and body contours have an omega (Omega) shape is made. In order to separate the background from these omega shapes, the three basic features of gray, color and edge are used, and combined. The skin color cue is very useful when the image stream is frontal and has large face regions, and additionally has no background objects similar to the skin color. The gray cue is also important when captured faces have a lower gray level than background objects. The edge cue is helpful when captured background objects have similar gray levels and colors to those of a head, but can be discriminated by edges. Since these three methods have roughly orthogonal failure results, they serve to complement each other. The next step is a splitting method between the head and body region using the proposed method. The final step is an ellipse fitting and a head region verification algorithm. The results of this algorithm provide robustness for head rotation, illumination changing, and variable head sizes. Furthermore, it is possible to carry out real time processing