Abstract
Three dimensional human motions recorded by motion capture and hand gestures recorded by using data gloves generate variable-length data streams. These data streams usually have dozens of attributes, and have different variations for similar motions. To segment and recognize motion streams, a classification-based approach is proposed in this paper. Classification feature vectors are extracted by utilizing singular value decompositions (SVD) of motion data. The extracted feature vectors capture the dominating geometric structures of motion data as revealed by SVD. Multi-class support vector machine (SVM) classifiers with class probability estimates are explored for classifying the feature vectors in order to segment and recognize motion streams. Experiments show that the proposed approach can find patterns in motion data streams with high accuracy.
Similar content being viewed by others
References
Burges CJ (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2:121–167
Dyaberi VM, Sundaram H, James J, Qian G (2004) Phrase structure detection in dance. In: Proceedings of the ACM Multimedia Conference 2004, pp 332–335
Ganapathiraju A, Hamaker JE, Picone J (2004) Application of support vector machines to speech recognition. IEEE Trans Signal Process 52(8):2348–2355
Golub GH, Loan CFV (1996) Matrix computations. The Johns Hopkins University Press, Baltimore, MD
Gordan M, Kotropoulos C, Pitas I (2002) Application of support vector machines classifiers to visual speech recognition. In: Proceedings of the International Conference on Image Processing, pp 24–28
Hsu C-W, Lin C-J (2002) A comparison of methods for multi-class support vector machines. IEEE Trans Neural Netw 13:415–425
Kahol K, Tripathi P, Panchanathan S, Rikakis T (2003) Gesture segmentation in complex motion sequences. In: Proceedings of IEEE International Conference on Image Processing II, pp 105–108
Krzanowski W (1979) Between-groups comparison of principal components. J Amer Statist Assoc 74(367):703–707
Li C, Khan L, Prabhakaran B (2006) Real-time classification of variable length multi-attribute motion data. Knowl Inf Syst 10(2):163–183
Li C, Prabhakaran B (2005) A similarity measure for motion stream segmentation and recognition. In: Proceedings of the Sixth International Workshop on Multimedia Data Mining
Li C, Prabhakaran B, Zheng S (2005) Similarity measure for multi-attribute data. In: Proceedings of the 2005 IEEE International Conference on Acoustics, Speach, and Signal Processing (ICASSP)
Li C, Zhai P, Zheng S-Q, Prabhakaran B (2004) Segmentation and recognition of multi-attribute motion sequences. In: Proceedings of the ACM Multimedia Conference 2004, pp 836–843
Natsev A, Naphade MR, Smith JR (2004) Semantic representation, search and mining of multimedia content. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 641–646
Platt JC (2000) Probabilistic outputs for support vector machines and comparison to regularized likelihood methods. In: Advances in Large Margin Classifiers, Smola AJ, Bartlett PL, Scholkopf B, Schuurmans D (eds) MIT Press, Cambridge, MA. http//:www.citeseer.nj.nec.com/platt99probabilistic.html
Qian G, Guo F, Ingalls T, Olson L, James J, Rikakis T (2004) A gesture-driven multimodal interactive dance system. In: Proceedings of IEEE International Conference on Multimedia and Expo
Shahabi C, Kaghazian L, Mehta S, Ghoting A, Shanbhag G, McLaughlin M (2001) Analysis of haptic data for sign language recognition. In: Proceedings of the 9th International Conference on Human Computer Interaction, pp 441–445
Shahabi C, Yan D (2003) Real-time pattern isolation and recognition over immersive sensor data streams. In: Proceedings of the 9th International Conference on Multi-media Modeling, pp 93–113
Starner T, Weaver J, Pentland A (1998) Real-time american sign language recognition using desk and wearable computer based video. IEEE Trans Pattern Anal Mach Intell 20(12):1371–1375
Stewart GW (1973) Error and perturbation bounds for subspace associated with certain eigenvalue problems. SIAM Rev 15(4):727–764
Vapnik VN (1998) Statistical learning theory. Wiley, New York
Vlachos M, Gunopulos D, Das G (2004) Rotation invariant distance measures for trajectories. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 707–712
Wu T-F, Liu C-J, Weng RC (2004) Probability estimates for multi-class classification by pairwise coupling. J Mach Learn Res 5:975–1005
Yang K, Shahabi C (2004) A PCA-based similarity measure for multivariate time series. In: Proceedings of the Second ACM International Workshop on Multimedia Databases, pp 65–74
Author information
Authors and Affiliations
Corresponding author
Additional information
Permission to make digital/hard copy of all or part of this material without fee for personal
or classroom use provided that the copies are not made or distributed for profit or commercial advantage.
Rights and permissions
About this article
Cite this article
Li, C., Kulkarni, P.R. & Prabhakaran, B. Segmentation and recognition of motion capture data stream by classification. Multimed Tools Appl 35, 55–70 (2007). https://doi.org/10.1007/s11042-007-0119-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-007-0119-6