Abstract
In order to evaluate workers’ progress in the factory, we developed a model of operation estimation method based on tracking records captured by Kinect. In this paper, we use Kinect sensors to capture the human motion process (such as broadcast gymnastics), extract the skeleton frame sequence from training data as a template, then improve the DTW algorithm to match the skeleton frame in the test data and in the training data, to estimate the percentage of the action’s completion. The improved DTW algorithm achieves state-of-the-art performances on our dataset, higher than 90%.
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References
Efrat, A., Fan, Q., Venkatasubramanian, S.: Curve matching, time warping, and light fields: new algorithms for computing similarity between curves. J. Math. Imaging Vis. 27(3), 203–216 (2007). http://dx.doi.org/10.1007/s10851-006-0647-0
Tappert, C.C., Suen, C.Y., Wakahara, T.: The state of the art in online handwriting recognition. IEEE Trans. Pattern Anal. Mach. Intell. 12(8), 787–808 (1990). http://dx.doi.org/10.1109/34.57669
Kuzmanic, A., Zanchi, V.: Hand shape classification using DTW and LCSS as similarity measures for vision-based gesture recognition system. In: EUROCON, 2007. The International Conference on Computer as a Tool 2007, pp. 264–269. http://dx.doi.org/10.1109/EURCON.2007.4400350
Corradini, A.: Dynamic time warping for off-line recognition of a small gesture vocabulary. In: RATFG-RTS 2001: Proceedings of the IEEE ICCV Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems (RATFG-RTS’01). IEEE Computer Society, Washington, D.C. (2001). http://portal.acm.org/citation.cfm?id=882476.883586
Niennattrakul, V., Ratanamahatana, C.A.: On clustering multimedia time series data using k-means and dynamic time warping. In: International Conference on Multimedia and Ubiquitous Engineering, MUE 2007, pp. 733–738 (2007). http://dx.doi.org/10.1109/MUE.2007.165
Gu, J., Jin, X.: A simple approximation for dynamic time warping search in large time series database, pp. 841–848 (2006). http://dx.doi.org/10.1007/11875581101
Bahlmann, C., Burkhardt, H.: The writer independent online handwriting recognition system frog on hand and cluster generative statistical dynamic time warping. IEEE Trans. Pattern Anal. Mach. Intell. 26(3), 299–310 (2004). http://dx.doi.org/10.1109/TPAMI.2004.1262308
Kahveci, T., Singh, A.: Variable length queries for time series data. In: 17th International Conference on Data Engineering, 2001. Proceedings, pp. 273–282 (2001). http://dx.doi.org/10.1109/ICDE.2001.914838
Kahveci, T., Singh, A., Gurel, A.: Similarity searching for multiattribute sequences. In: 14th International Conference on Scientific and Statistical Database Management, 2002. Proceedings, pp. 175–184 (2002). http://dx.doi.org/10.1109/SSDM.2002.1029718
Euachongprasit, W., Ratanamahatana, C.: Efficient multimedia time series data retrieval under uniform scaling and normalisation, pp. 506–513 (2008). http://dx.doi.org/10.1007/978-3-540-78646-749
Dtw-based motion comparison and retrieval, pp. 211–226 (2007). http://dx.doi.org/10.1007/978-3-540-74048-310
Zhang, Z., Huang, K., Tan, T.: Comparison of similarity measures for trajectory clustering in outdoor surveillance scenes. In: ICPR 2006: Proceedings of the 18th International Conference on Pattern Recognition (ICPR 2006), pp. 1135–1138. IEEE Computer Society, Washington, D.C. (2006). http://dx.doi.org/10.1109/ICPR.2006.392
Vial, J., Nocairi, H., Sassiat, P., Mallipatu, S., Cognon, G., Thiebaut, D., Teillet, B., Rutledge, D.: Combination of dynamic time warping and multivariate analysis for the comparison of comprehensive two-dimensional gas chromatograms application to plant extracts. J. Chromatogr. A (2008). http://dx.doi.org/10.1016/j.chroma.2008.09.027
Muller, M., Mattes, H., Kurth, F.: An efficient multiscale approach to audio synchronization, pp. 192–197 (2006)
Dynamic time warping, pp. 69–84 (2007). http://dx.doi.org/10.1007/978-3-540-74048-34
Acknowledgment
This project was completed with the Mitsubishi Heavy Industries cooperation, and was funded by the Mitsubishi Heavy Industries research project No.14-36.
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Wang, B. et al. (2017). Development of Operation Estimation Method Based on Tracking Records Captured by Kinect. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2016. Communications in Computer and Information Science, vol 710. Springer, Singapore. https://doi.org/10.1007/978-981-10-5230-9_15
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DOI: https://doi.org/10.1007/978-981-10-5230-9_15
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