Advanced SearchSearch Tips
Three-dimensional Head Tracking Using Adaptive Local Binary Pattern in Depth Images
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Three-dimensional Head Tracking Using Adaptive Local Binary Pattern in Depth Images
Kim, Joongrock; Yoon, Changyong;
  PDF(new window)
Recognition of human motions has become a main area of computer vision due to its potential human-computer interface (HCI) and surveillance. Among those existing recognition techniques for human motions, head detection and tracking is basis for all human motion recognitions. Various approaches have been tried to detect and trace the position of human head in two-dimensional (2D) images precisely. However, it is still a challenging problem because the human appearance is too changeable by pose, and images are affected by illumination change. To enhance the performance of head detection and tracking, the real-time three-dimensional (3D) data acquisition sensors such as time-of-flight and Kinect depth sensor are recently used. In this paper, we propose an effective feature extraction method, called adaptive local binary pattern (ALBP), for depth image based applications. Contrasting to well-known conventional local binary pattern (LBP), the proposed ALBP cannot only extract shape information without texture in depth images, but also is invariant distance change in range images. We apply the proposed ALBP for head detection and tracking in depth images to show its effectiveness and its usefulness.
Head detection;Local binary pattern;Feature extraction;Depth images;Kinect sensor;
 Cited by
J. K. Aggarwal and Q. Cai, "Human motion analysis: a review," in Proceedings of IEEE Nonrigid and Articulated Motion Workshop, San Juan, Puerto Rico, 1997, pp. 90-102. crossref(new window)

R. T. Collins, A. J. Lipton, T. Kanade, H. Fujiyoshi, D. Duggins, Y. Tsin, et al., "A system for video surveillance and monitoring," Carnegie Mellon University, Pittsburgh, PA, 2000.

I. Haritaoglu, D. Harwood, and L. S. Davis, "W4: realtime surveillance of people and their activities," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 809-830, 2000. crossref(new window)

H. Wang, J. Chen, B. Fang, and S. Dai, "Human detection algorithm based on edge symmetry," in Robot Intelligence Technology and Applications 3, J. H. Kim, W. Yang, J. Jo, P. Sincak, and H. Myung, Eds. Cham: Springer International Publishing, 2015, pp. 719-729. 65

W. R. Schwartz, A. Kembhavi, D. Harwood, and L. S. Davis, "Human detection using partial least squares analysis," in Proceedings of 2009 IEEE 12th International Conference on Computer Vision, Kyoto, Japan, 2009, pp. 24-31.

Z. Lin, L. S. Davis, D. Doermann, and D. DeMenthon, "Hierarchical part-template matching for human detection and segmentation," in Proceedings of 2007 IEEE 11th International Conference on Computer Vision, Rio de Janeiro, Brazil, 2007, pp. 1-8.

A. Kolb, E. Barth, R. Koch, and R. Larsen, "Time-offlight sensors in computer graphics," in Proceedings of the 30th Annual Conference of the European Association for Computer Graphics, Munich, Germany, 2009.

Y. Cui, S. Schuon, D. Chan, S. Thrun, and C. Theobalt, "3D shape scanning with a time-of-flight camera," in Proceedings of 2010 IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, 2010, pp. 1173-1180. crossref(new window)

Microsoft, "Kinect," Available

L. Xia, C.C. Chen, and J. K. Aggarwal, "Human detection using depth information by Kinect," in Proceedings of 2011 Computer Vision and Pattern Recognition Workshops, Colorado Springs, CO, 2011, pp. 15-22. http: // crossref(new window)

Z. Ren, J. Meng, and J. Yuan, "Depth camera based hand gesture recognition and its applications in Human-Computer-Interaction," in Proceedings of 2011 8th International Conference on Information, Communications and Signal Processing, Singapore, 2011, pp. 1-5. crossref(new window)

T. Ahonen, A. Hadid, and M. Pietikainen, "Face description with local binary patterns: application to face recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 2037-2041, 2006. crossref(new window)

T. Ojala, M. Pietikainen, and T. Maenpaa, "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971-987, 2002. crossref(new window)

M. Heikkila, M. Pietikainen, and C. Schmid, "Description of interest regions with local binary patterns," Pattern Recognition, vol. 42, no. 3, pp. 425-436, 2009. crossref(new window)

N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, 2005, pp. 886-893. crossref(new window)

Q. Zhu, M. C. Yeh, K. T. Cheng, and S. Avidan, "Fast human detection using a cascade of histograms of oriented gradients," in Proceedings of 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, New York, NY, 2006, pp. 1491-1498. crossref(new window)

D. G. Lowe, "Distinctive image features from scaleinvariant keypoints," International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, 2004. crossref(new window)

O. Biglari, R. Ahsan, and M. Rahi, "Human detection using SURF and SIFT feature extraction methods in different color spaces," Journal of Mathematics and Computer Science, vol. 11, pp. 111-122, 2014. crossref(new window)