Face Detection and Tracking for Human Robot Interaction through Service Robot

In our approach, a method is proposed to accomplish face detection and human tracking. In the realtime application such as robot system, the initialization and lost track problems often limit the application. The method which combines with face detection algorithm based on AdaBoost and the object tracking method using Kalman filter is proposed in this paper. To carry out the initialization problem, the global AdaBoost face detection (GAFD) algorithm is applied. It is powerful to detect multiple faces in the whole image. If there is a new face in image, a new face tracker and the new local AdaBoost face detection (LAFD) are both generated. The tracker will predict the possible area of the face appearing. According to the prediction of the tracker, partial image which is called region of interest (ROI) is obtained for LAFD. In the experimental result, our approach has been successfully implemented and test on service robot for human tracking. It is useful for the application of human-robot interaction.

[1]  Javier Minguez,et al.  Nearness diagram (ND) navigation: collision avoidance in troublesome scenarios , 2004, IEEE Transactions on Robotics and Automation.

[2]  Tomaso A. Poggio,et al.  A general framework for object detection , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[3]  Alexandros Eleftheriadis,et al.  Automatic face location detection and tracking for model-assisted coding of video teleconferencing sequences at low bit-rates , 1995, Signal Process. Image Commun..

[4]  Bernhard Fröba,et al.  Face Tracking by Means of Continuous Detection , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[5]  Alex Waibel,et al.  Tracking Human Faces in Real-Time, , 1995 .

[6]  Narendra Ahuja,et al.  Detecting Faces in Images: A Survey , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Myung Hwangbo,et al.  A stable target-tracking control for unicycle mobile robots , 2000, Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113).

[8]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[9]  Grzegorz Cielniak,et al.  Active people recognition using thermal and grey images on a mobile security robot , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Alexander H. Waibel,et al.  A real-time face tracker , 1996, Proceedings Third IEEE Workshop on Applications of Computer Vision. WACV'96.

[12]  Hanqi Zhuang,et al.  Real-time eye feature tracking from a video image sequence using Kalman filter , 1994, Conference Record Southcon.

[13]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[14]  Alex Pentland,et al.  Pfinder: real-time tracking of the human body , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[15]  Myung Jin Chung,et al.  Robust multi-view face tracking , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[16]  Hisato Kobayashi,et al.  Moving object detection by an autonomous guard robot , 1995, Proceedings 4th IEEE International Workshop on Robot and Human Communication.