TY - GEN
T1 - A head pose tracking system using RGB-D camera
AU - Li, Songnan
AU - Ngan, King Ngi
AU - Sheng, Lu
PY - 2013
Y1 - 2013
N2 - In this paper, a fast head pose tracking system is introduced. It uses iterative closest point algorithm to register a dense face template to depth data captured by Kinect. It can achieve 33fps processing speed without specific optimization. To improve tracking robustness, head movement prediction is applied. We propose a novel scheme that can train several simple predictors together, enhancing the overall prediction accuracy. Experimental results confirm its effectiveness for head movement prediction.
AB - In this paper, a fast head pose tracking system is introduced. It uses iterative closest point algorithm to register a dense face template to depth data captured by Kinect. It can achieve 33fps processing speed without specific optimization. To improve tracking robustness, head movement prediction is applied. We propose a novel scheme that can train several simple predictors together, enhancing the overall prediction accuracy. Experimental results confirm its effectiveness for head movement prediction.
KW - head movement prediction
KW - head pose tracking
KW - iterative closest point algorithm
KW - K-means like retraining
UR - https://www.scopus.com/pages/publications/84881228669
U2 - 10.1007/978-3-642-39402-7_16
DO - 10.1007/978-3-642-39402-7_16
M3 - 会议稿件
AN - SCOPUS:84881228669
SN - 9783642394010
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 153
EP - 162
BT - Computer Vision Systems - 9th International Conference, ICVS 2013, Proceedings
T2 - 9th International Conference on Computer Vision Systems, ICVS 2013
Y2 - 16 July 2013 through 18 July 2013
ER -