Abstract
Automatic tracking of the articulations of human from avideo sequence is a difficult task due to complex motions of the limbs, dynamic background, and varieties of poses. These challenges make it difficult to train a generative motion and appearance model to be used in different scenarios. In our work, we employ particle swarm optimization framework to avoid the need of motion model. Particularly, we propose a novel appearance learning strategy to learn the appearance of each body part in real time. Furthermore, we also propose an appearance model to represent the shape of each body part. Samples from UIUC dataset had been used in experiments. The results had shown that our method performed well on complex activities without motion model and online appearance training. It also showed the robustness of our method to recover from tracking failure in an occluded video.
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
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bregler, C., Malik, J.: Tracking People with Twists and Exponential Maps. In: CVPR, pp. 8–15 (1998)
Sidenbladh, H., Black, M.J., Fleet, D.J.: Stochastic Tracking of 3D Human Figures using 2D Image Motion. In: Vernon, D. (ed.) ECCV 2000, Part II. LNCS, vol. 1843, pp. 702–718. Springer, Heidelberg (2000)
Balan, A.O., Sigal, L., Black, M.J.: A Quantitative Evaluation of Video-based 3D Person Tracking. In: ICCCN, pp. 349–356 (2005)
Deutscher, J., Reid, I.: Articulated Body Motion Capture by Stochastic Search. Int. Journal of Computer Vision 61, 185–205 (2005)
Chan, C.S., Liu, H., Brown, D.J., Kubota, N.: A Fuzzy Qualitative Approach to Human Motion Recognition. In: FUZZ-IEEE, pp. 1242–1249 (2008)
Ivekovic, S., John, V., Trucco, E.: Markerless Multi-view Articulated Pose Estimation using Adaptive Hierarchical Particle Swarm Optimisation. In: Di Chio, C., et al. (eds.) EvoApplicatons 2010, Part I. LNCS, vol. 6024, pp. 241–250. Springer, Heidelberg (2010)
John, V., Trucco, E., Ivekovic, S.: Markerless Human Articulated Tracking using Hierarchical Particle Swarm Optimisation. Image and Vision Computing 28, 1530–1547 (2010)
Chan, C.S., Liu, H.: Fuzzy Qualitative Human Motion Analysis. IEEE Transactions on Fuzzy Systems 17(4), 851–862 (2009)
Sidenbladh, H., Black, M.J., Sigal, L.: Implicit Probabilistic Models of Human Motion for Synthesis and Tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part I. LNCS, vol. 2350, pp. 784–800. Springer, Heidelberg (2002)
Ikizler, N., Forsyth, D.: Searching for Complex Human Activities with No Visual Examples. Int. Journal of Comp. Vision 80, 337–357 (2008)
Yang, Y., Ramanan, D.: Articulated Human Detection with Flexible Mixtures of Parts. IEEE Transactions on Pattern Anal. and Machine Intelligence (in press)
Ramanan, D., Forsyth, D., Zisserman, A.: Tracking People by Learning Their Appearance. IEEE Transactions on Pattern Anal. and Machine Intelligence 29, 65–81 (2007)
Cifuentes, C.G., Sturzel, M., Jurie, F., Brostow, G.: Motion Models that Only Work Sometimes. In: BMVC, Surrey, pp. 55.1–55.12 (2012)
Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: CVPR, San Diego, pp. 886–893 (2005)
Czyz, J., Ristic, B., Macq, B.: A Particle Filter for Joint Detection and Tracking of Color Object. Image and Vision Computing 25, 1271–1281 (2007)
Tan, W.R., Chan, C.S., Yogarajah, P., Condell, J.: A Fusion Approach for Efficient Human Skin Detection. IEEE Transactions on Industrial Informatics 8(1), 138–147 (2012)
Plänkers, R., Fua, P.: Model-based Silhouette Extraction for Accurate People Tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part II. LNCS, vol. 2351, pp. 325–339. Springer, Heidelberg (2002)
Balan, A.O., Black, M.J.: An Adaptive Appearance Model Approach for Model-based Articulated Object Tracking. In: CVPR, New York, pp. 758–765 (2006)
Chan, C.S., Liu, H., Lai, W.K.: Fuzzy Qualitative Complex Actions Recognition. In: FUZZ-IEEE, Barcelona, pp. 1–8 (2008)
Jepson, A., Fleet, D., El-Maraghi, T.: Robust Online Appearance Models for Visual Tracking. IEEE Transactions on Pattern Anal. and Machine Intelligence 25, 1296–1311 (2003)
Kalal, Z., Mikolajczyk, K., Matas, J.: Face-tld: Tracking-learning-detection Applied to Faces. In: ICIP, Hong Kong, pp. 3789–3792 (2010)
Piccardi, M.: Background Subtraction Techniques: A Review. In: IEEE SMC, The Hague, vol. 4, pp. 3099–3104 (2004)
Chutatape, O., Guo, L.: A Modified Hough Transform for Line Detection and Its Performance. Pattern Recognition 32, 181–192 (1999)
Beauchemin, S.S., Barron, J.L.: The Computation of Optical Flow. ACM Computing Surveys 27, 433–466 (1995)
Helwig, S., Wanka, R.: Particle Swarm Optimization in High-dimentional Bounded Search Spaces. In: SIS, Honolulu, pp. 198–205 (2007)
Lim, M.K., Chan, C.S., Monekosso, D., Remagnino, P.: SwATrack: A Swarm Intelligence-based Abrupt Motion Tracker. In: IAPR MVA, Kyoto (2013)
Lu, H., Yen, G.: Dynamic Population Size in Multiobjective Evolutionary Algorithms. In: CEC, Hawaii, vol. 2, pp. 1648–1653 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tan, W.R., Chan, C.S., Aguirre, H.E., Tanaka, K. (2013). Articulated Human Motion Tracking with Online Appearance Learning. In: Noah, S.A., et al. Soft Computing Applications and Intelligent Systems. M-CAIT 2013. Communications in Computer and Information Science, vol 378. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40567-9_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-40567-9_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40566-2
Online ISBN: 978-3-642-40567-9
eBook Packages: Computer ScienceComputer Science (R0)