Abstract
This paper presents a novel and fast scheme to detect different body parts in human motion. Using monocular video sequences, trajectory estimation and body modeling of moving humans are combined in a co-operating processing architecture. More specifically, for every individual person, features of body ratio, silhouette and appearance are integrated into a hybrid model to detect body parts. The conventional assumption of upright body posture is not required. We also present a new algorithm for accurately finding the center point of the human body. The body configuration is finally described by a skeleton model. The feasibility and accuracy of the proposed scheme are analyzed by evaluating its performance for various sequences with different subjects and motion types (walking, pointing, kicking, leaping and falling). Our detection system achieves nearly real-time performance (around 10 frames/second).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Moeslund, T.B., Hilton, A., Kruger, V.: A Survey of Advances in Vision-Based Human Motion Capture and Analysis. Computer Vision and Image Understanding 104, 90–126 (2006)
Lao, W., Han, J., de With, P.H.N.: A Matching-Based Approach for Human Motion Analysis. In: Cham, T.-J., Cai, J., Dorai, C., Rajan, D., Chua, T.-S., Chia, L.-T. (eds.) MMM 2007. LNCS, vol. 4352, pp. 405–414. Springer, Heidelberg (2006)
Viola, P., Jones, M., Snow, D.: Detecting Pedestrians Using Patterns of Motion and Appearance. In: Proc. Int. Conf. Computer Vision, pp. 734–741 (2003)
Aggarwal, K.: Simultaneous Tracking of Multiple Body Parts of Interacting Persons. Computer Vision and Image Understanding 102, 1–21 (2006)
Fujiyoshi, H., Lipton, A., Kanade, T.: Real-time Human Motion Analysis by Image Skeletonization. IEICE Trans. Information and System 87, 113–120 (2004)
Haritaoglu, I., Harwood, D., Davis, L.: W4: Real-Time Surveillance of People and Their Activities. IEEE Trans. Pattern Analysis and Machine Intelligence 22, 809–830 (2000)
Yu, C., Hwang, J., Ho, G., Hsieh, C.: Automatic Human body Tracking and Modeling from Monocular Video Sequences. In: IEEE Proc. Int. Conf. Acoustics, Speech and Signal Processing, Hawaii, vol. 1, pp. 917–920 (2007)
Peursum, P., Bui, H., Venkatesh, S., West, G.: Robust Recognition and Segmentation of Human Actions Using HMMs with Missing Observations. EURASIP Journal on Applied Signal Processing 13, 2110–2126 (2005)
Zivkovic, Z., van der Heijden, F.: Efficient Adaptive Density Estimation per Image Pixel for the Task of Background Subtraction. Pattern Recognition Letters 27, 773–780 (2006)
Han, J., Farin, D., de With, P.H.N., Lao, W.: Real-Time Video Content Analysis Tool for Consumer Media Storage System. IEEE Trans. Consumer Electronics 52, 870–878 (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lao, W., Han, J., de With, P.H.N. (2008). Fast Detection and Modeling of Human-Body Parts from Monocular Video. In: Perales, F.J., Fisher, R.B. (eds) Articulated Motion and Deformable Objects. AMDO 2008. Lecture Notes in Computer Science, vol 5098. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70517-8_37
Download citation
DOI: https://doi.org/10.1007/978-3-540-70517-8_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70516-1
Online ISBN: 978-3-540-70517-8
eBook Packages: Computer ScienceComputer Science (R0)