Abstract
In this paper, we propose Markov random fields (MRFs) to automatically detect a moving human body through minimizing the joint energy of the MRF for the velocity and relative position of body parts. The relaxation labeling algorithm is employed to find the best body part labeling configuration between MRFs and observed data. We detect a walking motion viewed monocularly based on point features, where some points are from the unoccluded body parts and some belong to the background. The results show that MRFs can detect human motions robustly and accurately.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
We denote V(o i |f i = j) as V(o i |j).
References
Song Y, Goncalves L, Perona P (2003) Unsupervised learning of human motion. IEEE Trans Pattern Anal Mach Intell 25(7):1–14
Yu Q, Medioni G (2009) Multiple target tracking by spatio-temporal Monte Carlo Markov chain data association. IEEE Trans Pattern Anal Mach Intell 31(12):2196–2210
Isard M, Blake A (1998) Condensation-conditional density propagation for visual tracking. Int J Comput Vis 29(1):5–28
Lee MW, Nevatia R (2009) Human pose tracking in monocular sequence using multilevel structured models. IEEE Trans Pattern Anal Mach Intell 31(1):27–38
Wu Y, Yu T (2006) A field model for human detection and tracking. IEEE Trans Pattern Anal Mach Intell 28(5):753–765
Khan Z, Balch T, Dellaert F (2005) MCMC-based particle filtering for tracking a variable number of interacting targets. IEEE Trans Pattern Anal Mach Intell 27(11):1805–1819
Magnenat-Thalmann N, Thalmann D (1991) Complex models for animating synthetic actors. IEEE Comput Graph Appl 11(5):32–44
Johansson G (1973) Visual perception of biological motion and a model for its analysis. Percept Psychophys 14:201–211
Biederman I (1987) Recognition-by-components: a theory of human image understanding. Psychol Rev 94(2):115–147
Moeslund TB, Granum E (2001) A survey of computer vision-based human motion capture. Comput Vis Image Underst 81:231–268
Moeslund TB, Hilton A, Kruger V (2006) A survey of advances in vision-based human motion capture and analysis. Comput Vis Image Underst 104:90–126
Li SZ (2001) Markov random field modeling in image analysis. Springer, Tokyo
Zeng J, Liu Z-Q (2008a) Markov random field-based statistical character structure modeling for handwritten Chinese character recognition. IEEE Trans Pattern Anal Mach Intell 30(5):767–780
Zeng J, Liu ZQ (2008b) Type-2 fuzzy Markov random fields and their application to handwritten Chinese character recognition. IEEE Trans Fuzzy Syst 16(3):747–760
Zeng J, Feng W, Xie L, Liu Z-Q (2010) Cascade markov random fields for stroke extraction of chinese characters. Inf Sci 180:301–311
Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv 38(4):13
Kang J, Cohen I, Medioni G (2004) Object reacquisition using invariant appearance model. In: IEEE International Conference on Pattern Recognition (ICPR), pp 759–762
Chen Y, Rui Y, Huang TS (2001) JPDAF based HMM for real-time contour tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 543–550
Haritaoglu I, Harwood D, Davis LS (2000) w 4: Real-time surveillance of people and their activities. IEEE Trans Pattern Anal Mach Intell 22(8):809–830
Mansouri AR (2002) Region tracking via level set PDEs without motion computation. IEEE Trans Pattern Anal Mach Intell 24(7):947–961
Bertalmio M, Sapiro G, Randall G (2000) Morphing active contours. IEEE Trans Pattern Anal Mach Intell 22(7):733–737
Lan SF, Ho MF, Huang CL (2008) Human motion parameter capturing using particle filter and nonparametric belief propagation. In: IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), pp 37–40
Wu Y, Hua G, Yu T (2003) Tracking articulated body by dynamic markov network. In: IEEE International Conference on Computer Vision (ICCV), pp 1094–1101
Hua G, Yang MH, Wu Y (2005) Learning to estimate human pose with data driven belief propagation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 747–754
Han TX, Ning H, Huang TS (2006) Efficient nonparametric belief propagation with application to articulated body tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 214–221
Sigal L, Bhatia S, Roth S, Black MJ, Isard M (2004) Tracking loose-limbed people. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 421–428
Tomasi C, Kanade T (1991) Detection and tracking of point features, Technical Report CMU-CS-91-132, Carnegie Mellon Univ
Lowe D (2004) Distinctive image features form scale-invariant keypoints. Int J Comput Vis 60(2):91–110
Acknowledgments
The work described in this paper was partial supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. CityU 118608).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Cao, XQ., Liu, ZQ. Human motion detection using Markov random fields. J Ambient Intell Human Comput 1, 211–220 (2010). https://doi.org/10.1007/s12652-010-0015-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-010-0015-1