Abstract
Motion capture data digitally represent human movements by sequences of body configurations in time. Searching in such spatio-temporal data is difficult as query-relevant motions can vary in lengths and occur arbitrarily in the very long data sequence. There is also a strong requirement on effective similarity comparison as the specific motion can be performed by various actors in different ways, speeds or starting positions. To deal with these problems, we propose a new subsequence matching algorithm which uses a synergy of elastic similarity measure and multi-level segmentation. The idea is to generate a minimum number of overlapping data segments so that there is at least one segment matching an arbitrary subsequence. A non-partitioned query is then efficiently evaluated by searching for the most similar segments in a single level only, while guaranteeing a precise answer with respect to the similarity measure. The retrieval process is efficient and scalable which is confirmed by experiments executed on a real-life dataset.
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Barbič, J., Safonova, A., Pan, J.Y., Faloutsos, C., Hodgins, J.K., Pollard, N.S.: Segmenting motion capture data into distinct behaviors. In: Graphics Interface, pp. 185–194. Canadian Human-Computer Communications Society (2004)
Beecks, C., Hassani, M., Obeloer, F., Seidl, T.: Efficient query processing in 3D motion capture databases via lower bound approximation of the gesture matching distance. In: 2015 IEEE International Symposium on Multimedia (ISM 2015), pp. 148–153 (2015)
Bouchard, D., Badler, N.I.: Semantic segmentation of motion capture using Laban movement analysis. In: Pelachaud, C., Martin, J.-C., André, E., Chollet, G., Karpouzis, K., Pelé, D. (eds.) IVA 2007. LNCS (LNAI), vol. 4722, pp. 37–44. Springer, Heidelberg (2007)
Elias, P., Sedmidubsky, J., Zezula, P.: Motion images: an effective representation of motion capture data for similarity search. In: Amato, G., et al. (eds.) SISAP 2015. LNCS, vol. 9371, pp. 250–255. Springer, Heidelberg (2015). doi:10.1007/978-3-319-25087-8_24
Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time-series databases. SIGMOD Rec. 23(2), 419–429 (1994)
Kapadia, M., Chiang, I.K., Thomas, T., Badler, N.I., Kider Jr., J.T.: Efficient motion retrieval in large motion databases. In: ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games (I3D 2013), pp. 19–28. ACM (2013)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105. Curran Associates, Inc. (2012)
Krüger, B., Tautges, J., Weber, A., Zinke, A.: Fast local and global similarity searches in large motion capture databases. In: ACM Symposium on Computer Animation, SCA 2010, pp. 1–10. Eurographics Association (2010)
Lan, R., Sun, H.: Automated human motion segmentation via motion regularities. Vis. Comput. 31(1), 35–53 (2015)
Müller, M., Röder, T., Clausen, M., Eberhardt, B., Krüger, B., Weber, A.: Documentation Mocap database HDM05. Technical report CG-2007-2. Universität Bonn (2007)
Müller, M., Baak, A., Seidel, H.P.: Efficient and Robust annotation of motion capture data. In: ACM Symposium on Computer Animation (SCA 2009), p. 10. ACM Press (2009)
Novak, D., Cech, J., Zezula, P.: Efficient image search with neural net features. In: Amato, G., et al. (eds.) SISAP 2015. LNCS, vol. 9371, pp. 237–243. Springer, Heidelberg (2015). doi:10.1007/978-3-319-25087-8_22
Novak, D., Zezula, P.: Rank aggregation of candidate sets for efficient similarity search. In: Decker, H., Lhotská, L., Link, S., Spies, M., Wagner, R.R. (eds.) DEXA 2014, Part II. LNCS, vol. 8645, pp. 42–58. Springer, Heidelberg (2014)
Ren, C., Lei, X., Zhang, G.: Motion data retrieval from very large motion databases. In: International Conference on Virtual Reality and Visualization (ICVRV 2011), pp. 70–77 (2011)
Sedmidubsky, J., Valcik, J., Zezula, P.: A key-pose similarity algorithm for motion data retrieval. In: Blanc-Talon, J., Kasinski, A., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2013. LNCS, vol. 8192, pp. 669–681. Springer, Heidelberg (2013)
Valcik, J., Sedmidubsky, J., Zezula, P.: Assessing similarity models for human-motion retrieval applications. Computer Animation and Virtual Worlds (2015)
Vögele, A., Krüger, B., Klein, R.: Efficient unsupervised temporal segmentation of human motion. In: ACM Symposium on Computer Animation (2014)
Wang, Y., Neff, M.: Deep signatures for indexing and retrieval in large motion databases. In: 8th ACM Conference on Motion in Games, pp. 37–45. ACM (2015)
Wu, S., Wang, Z., Xia, S.: Indexing and retrieval of human motion data by a hierarchical tree. In: 16th ACM Symposium on Virtual Reality Software and Technology (VRST 2009), pp. 207–214. ACM, New York (2009)
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This research was supported by GBP103/12/G084.
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Sedmidubsky, J., Elias, P., Zezula, P. (2016). Similarity Searching in Long Sequences of Motion Capture Data. In: Amsaleg, L., Houle, M., Schubert, E. (eds) Similarity Search and Applications. SISAP 2016. Lecture Notes in Computer Science(), vol 9939. Springer, Cham. https://doi.org/10.1007/978-3-319-46759-7_21
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DOI: https://doi.org/10.1007/978-3-319-46759-7_21
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