Browse > Article
http://dx.doi.org/10.5909/JBE.2016.21.6.861

Real-time Face Tracking Method using Improved CamShift  

Lee, Jun-Hwan (Department of Electrical Engineering, KwangWoon University)
Yoo, Jisang (Department of Electrical Engineering, KwangWoon University)
Publication Information
Journal of Broadcast Engineering / v.21, no.6, 2016 , pp. 861-877 More about this Journal
Abstract
This paper first discusses the disadvantages of the existing CamShift Algorithm for real time face tracking, and then proposes a new Camshift Algorithm that performs better than the existing algorithm. The existing CamShift Algorithm shows unstable tracking when tracing similar colors in the background of objects. This drawback of the existing CamShift is resolved by using Kinect’s pixel-by-pixel depth information and the Skin Detection algorithm to extract candidate skin regions based on HSV color space. Additionally, even when the tracking object is not found, or when occlusion occurs, the feature point-based matching algorithm makes it robust to occlusion. By applying the improved CamShift algorithm to face tracking, the proposed real-time face tracking algorithm can be applied to various fields. The results from the experiment prove that the proposed algorithm is superior in tracking performance to that of existing TLD tracking algorithm, and offers faster processing speed. Also, while the proposed algorithm has a slower processing speed than CamShift, it overcomes all the existing shortfalls of the existing CamShift.
Keywords
Face tracking; Face-TLD; Haar-Feature; FAST; BRIEF; CamShift; Kinect;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Bhattacharyya, Anil. "On a measure of divergence between two multinomial populations." Sankhya: the indian journal of statistics (1946): 401-406.
2 Trzcinski, Tomasz, and Vincent Lepetit. "Efficient discriminative projections for compact binary descriptors." Computer Vision-ECCV 2012. Springer Berlin Heidelberg. pp. 228-242, 2012.
3 Danielsson, Per-Erik. "Euclidean distance mapping." Computer Graphics and image processing 14.3 (1980): 227-248.   DOI
4 Kyong-Ho Lee. "Face Tracking Using Face Feature and Color Information." Journal of the Korea Society of Computer and Information , 18.11 (2013.11): 167-174.
5 Viola, Paul, and Michael Jones. "Rapid object detection using a boosted cascade of simple features." Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on. vol. 1, pp. 511, 2001
6 Viola, Paul, and Michael Jones. "Fast and robust classification using asymmetric adaboost and a detector cascade." Advances in Neural Information Processing System 14 (2001).
7 Jones, Michael J., and James M. Rehg. "Statistical color models with application to skin detection." International Journal of Computer Vision 46.1 (2002): 81-96.   DOI
8 Vezhnevets, Vladimir, Vassili Sazonov, and Alla Andreeva. "A survey on pixel-based skin color detection techniques." Proc. Graphicon. vol. 3, pp.85-92, September, 2003.
9 Bradski, Gary R. "Computer vision face tracking for use in a perceptual user interface." (1998).
10 Allen, John G., Richard YD Xu, and Jesse S. Jin. "Object tracking using camshift algorithm and multiple quantized feature spaces." Proceedings of the Pan-Sydney area workshop on Visual information processing. Australian Computer Society, Inc., 2004.
11 Wang, Zhaowen, et al. "CamShift guided particle filter for visual tracking." Pattern Recognition Letters 30.4 (2009): 407-413.   DOI
12 Zhang, Zhengyou. "A flexible new technique for camera calibration." IEEE Transactions on pattern analysis and machine intelligence 22.11 (2000): 1330-1334.   DOI
13 Osuna, Edgar, Robert Freund, and Federico Girosit. "Training support vector machines: an application to face detection." Computer vision and pattern recognition, 1997. Proceedings., 1997 IEEE computer society conference on. IEEE, 1997.
14 Tsai, Roger. "A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses." IEEE Journal on Robotics and Automation 3.4 (1987): 323-344.   DOI
15 Viola, Paul, and Michael J. Jones. "Robust real-time face detection." International journal of computer vision 57.2 (2004): 137-154.   DOI
16 Rowley, Henry A., Shumeet Baluja, and Takeo Kanade. "Neural network-based face detection." IEEE Transactions on pattern analysis and machine intelligence 20.1 (1998): 23-38.   DOI
17 Hsu, Rein-Lien, Mohamed Abdel-Mottaleb, and Anil K. Jain. "Face detection in color images." IEEE transactions on pattern analysis and machine intelligence 24.5 (2002): 696-706.   DOI
18 Hjelmas, Erik, and Boon Kee Low. "Face detection: A survey." Computer vision and image understanding 83.3 (2001): 236-274.   DOI
19 Kalal, Zdenek, Krystian Mikolajczyk, and Jiri Matas. "Face-tld: Tracking-learning-detection applied to faces." Image Processing (ICIP), 2010 17th IEEE International Conference on. IEEE, 2010.
20 Kalal, Zdenek, Krystian Mikolajczyk, and Jiri Matas. "Tracking-learning-detection." Pattern Analysis and Machine Intelligence, IEEE Transactions on vol. 34, no. 7, pp. 1409-1422, 2012.   DOI
21 Young-Gon Kim, Rae-Hong Park, and Seong-Su Mun "Face Detection Using Adaboost and Template Matching of Depth Map based Block Rank Patterns",JBE, Vol. 17, No. 3, pp. 437-446, May 2012.
22 Hamming, Richard W. "Error detecting and error correcting codes." Bell System technical journal 29.2 (1950): 147-160.   DOI
23 Hoo Hyun Kim, Dong-Chan Cho, Jong Yeop Bae, Whoi-Yul Kim. "Rotation Invariant Face Detection with Boosted Random Ferns." The Korean Institute of Broadcast and Media Engineers Summer Conference, (2013.6): 52-55.
24 Muller, Meinard. "Dynamic time warping." Information retrieval for music and motion (2007): 69-84.
25 Lowe, David G. "Distinctive image features from scale-invariant keypoints."International journal of computer vision vol. 60, no. 2, pp. 91-110, 2004.   DOI
26 Harris, Chris, and Mike Stephens. "A combined corner and edge detector."Alvey vision conference. vol. 15. 1988.
27 Bay, Herbert, et al. "Speeded-up robust features (SURF)." Computer vision and image understanding vol. 110, no. 3, pp. 346-359, 2008.   DOI
28 Song, Shuran, and Jianxiong Xiao. "Tracking revisited using rgbd camera: Unified benchmark and baselines." Proceedings of the IEEE international conference on computer vision. 2013.
29 Fischler, Martin A., and Robert C. Bolles. "Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography." Communications of the ACM 24.6 (1981): 381-395.   DOI
30 http://tracking.cs.princeton.edu/index.html
31 http://darkpgmr.tistory.com/80
32 Weng, Juyang, Paul Cohen, and Marc Herniou. "Camera calibration with distortion models and accuracy evaluation." IEEE Transactions on pattern analysis and machine intelligence 14.10 (1992): 965-980.   DOI
33 Rosten, Edward, and Tom Drummond. "Machine learning for high-speed corner detection." Computer Vision-ECCV 2006. Springer Berlin Heidelberg. pp. 430-443, 2006.
34 Muhlmann, Karsten, et al. "Calculating dense disparity maps from color stereo images, an efficient implementation." International Journal of Computer Vision 47.1-3 (2002): 79-88.   DOI
35 Zhang, Zhengyou. "Microsoft kinect sensor and its effect." IEEE multimedia 19.2 (2012): 4-10.   DOI
36 Pagliari, Diana, and Livio Pinto. "Calibration of kinect for xbox one and comparison between the two generations of Microsoft sensors." Sensors 15.11 (2015): 27569-27589.   DOI
37 Calonder, Michael, et al. "Brief: Binary robust independent elementary features." Computer Vision-ECCV 2010 pp. 778-792, 2010.
38 https://www.flickr.com/photos/unavoidablegrain/6884354772/in/photostream/ (Image by Greg Borenstein)
39 Comaniciu, Dorin, and Peter Meer. "Mean shift: A robust approach toward feature space analysis." IEEE Transactions on pattern analysis and machine intelligence 24.5 (2002): 603-619.   DOI