Abstract
Human face detection is an essential area of computer vision that harness the digital vision for detecting, recognizing, and tracking human faces. Face detection and tracking is widely being used in different applications like security monitoring, home video surveillance, etc. This paper demonstrates an effective human face tracking system for a real-time video stream. In particular, we investigate the two widely used algorithms, MeanShift and CamShift algorithms, for face tracking. However, face detection is performed only in the first video frame using basic Haar cascade features and AdaBoost classifiers. The advantages and drawbacks of MeanShift and CamShift algorithms are discussed in detail with the help of real-time video frames. Results show that the CamShift algorithm is more relevant in head and face tracking, which makes the algorithm highly robust and practically adaptive.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cong, D., Shi, P., Zhou, D.: An improved camshift algorithm based on RGB histogram equalization. In: 2014 7th International Congress on Image and Signal Processing. pp. 426–430. IEEE (2014)
Cuimei, L., Zhiliang, Q., Nan, J., Jianhua, W.: Human face detection algorithm via haar cascade classifier combined with three additional classifiers. In: 2017 13th IEEE International Conference on Electronic Measurement and Instruments (ICEMI) (2017)
Hongling, W., Bo, Y., Guodong, T., Aidong, M.: Object tracking by applying mean-shift algorithm into particle filtering. In: 2009 2nd IEEE International Conference on Broadband Network and Multimedia Technology. pp. 550–554 (2009)
Chen, S., Ma, X., Zhang, S.: Adaboost face detection based on haar-like intensity features and multi-threshold features. In: 2011 International Conference on Multimedia and Signal Processing. vol. 1, pp. 251–255 (2011)
Viraktamath, S.V., Katti, M., Khatawkar, A., Kulkarni, P.: Face detection and tracking using openCV. SIJ Trans. Comput. Netw. Commun. Eng. 4(3), 1–6 (2016)
Cong, D., Shi, P., Zhou, D.: An improved camshift algorithm based on RGB histogram equalization. In: 2014 7th International Congress on Image and Signal Processing. pp. 426–430 (2014)
Viola, P., Jones, M.: Robust real-time face detection. In: Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001. vol. 57, pp. 137–154 (2001)
Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. In: Proceedings of the 11th Annual Conference on Computational Learning Theory, vol. 37, pp. 80–91 (1998)
Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: ICML’96 Proceedings of the Thirteenth International Conference on International Conference on Machine Learning. pp. 148–156 (1996)
Li, S., Zhang, Z.: Floatboost learning and statistical face detection. IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 1112–1123 (2004)
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–575 (2003)
Sun, L., Wang, B., Ikenaga, T.: Real-time non-rigid object tracking using camshift with weighted back projection. In: 2010 International Conference on Computational Science and Its Applications. pp. 86–91 (2010)
Bradski, G.R.: Computer vision face tracking for use in a perceptual user interface. In: WACV (1998)
Wilson, P.I., Fernandez, J.: Facial feature detection using haar classifiers. J. Comput. Sci. Coll. 21(4), 127–133 (2006)
Allen, J.G., Xu, R.Y.D., Jin, J.S.: Object tracking using camshift algorithm and multiple quantized feature spaces. VIP ’05 Proceedings of the Pan-Sydney Area Workshop on Visual information Processing pp. 3–7 (2004)
Kim, M., Kumar, S., Pavlovic, V., Rowley, H.: Face tracking and recognition with visual constraints in real-world videos. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition. pp. 1–8 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gupta, S., Kumar, K., Pal, S., Ghosh, K. (2021). A Comprehensive Study of MeanShift and CamShift Algorithms for Real-Time Face Tracking. In: Mishra, D., Buyya, R., Mohapatra, P., Patnaik, S. (eds) Intelligent and Cloud Computing. Smart Innovation, Systems and Technologies, vol 194. Springer, Singapore. https://doi.org/10.1007/978-981-15-5971-6_86
Download citation
DOI: https://doi.org/10.1007/978-981-15-5971-6_86
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-5970-9
Online ISBN: 978-981-15-5971-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)