Fast Human Activity Recognition Based on a Massively Parallel Implementation of Random Forest

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• 关键词：Random forests ; Classification ; GPU ; CUDA ; Parallelisation
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9622
• 期：1
• 页码：169-178
• 全文大小：362 KB
• 参考文献：1.Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine. In: Bravo, J., Hervás, R., Rodríguez, M. (eds.) IWAAL 2012. LNCS, vol. 7657, pp. 216–223. Springer, Heidelberg (2012)CrossRef
  2.Bańczyk, K., Kempa, O., Lasota, T., Trawiński, B.: Empirical comparison of bagging ensembles created using weak learners for a regression problem. In: Nguyen, N.T., Kim, C.-G., Janiak, A. (eds.) ACIIDS 2011, Part II. LNCS, vol. 6592, pp. 312–322. Springer, Heidelberg (2011)CrossRef
  3.Ben-Arie, J., Wang, Z., Pandit, P., Rajaram, S.: Human activity recognition using multidimensional indexing. IEEE Trans. Pattern Anal. Mach. Intell. 24(8), 1091–1104 (2002)CrossRef
  4.Bobick, A.F., Davis, J.W.: The recognition of human movement using temporal templates. IEEE Trans. Pattern Anal. Mach. Intell. 23(3), 257–267 (2001)CrossRef
  5.Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). http://​dx.​doi.​org/​10.​1023/​A%3A1010933404324
  6.Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)MathSciNet CrossRef MATH
  7.Cuzzocrea, A., Francis, S.L., Gaber, M.M.: An information-theoretic approach for setting the optimal number of decision trees in random forests. In: SMC, pp. 1013–1019 (2013)
  8.Dabney, A.R.: Classification of microarrays to nearest centroids. Bioinformatics 21(22), 4148–4154 (2005)CrossRef
  9.Denil, M., Matheson, D., de Freitas, N.: Narrowing the gap: Random forests in theory and in practice. CoRR abs/1310.1415 (2013)
  10.Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000)CrossRef
  11.Freund, Y.: An adaptive version of the boost by majority algorithm. Mach. Learn. 43(3), 293–318 (2001). http://​dx.​doi.​org/​10.​1023/​A%3A1010852229904
  12.Gehrke, J.: Classification and regression trees. In: Encyclopedia of Data Warehousing and Mining, pp. 192–195 (2009)
  13.Groupware@LES, R.G: Wearable computing: Classification of body postures and movements data set (2013). http://​groupware.​les.​inf.​puc-rio.​br/​har
  14.Hastie, T., Tibshirani, R.: Discriminant adaptive nearest neighbor classification. IEEE Trans. Pattern Anal. Mach. Intell. 18(6), 607–616 (1996)CrossRef
  15.Kirk, D., Hwu, W.: Programming Massively Parallel Processors: A Hands-on Approach. Applications of GPU Computing Series, Elsevier Science (2010). http://​books.​google.​cz/​books?​id=​qW1mncii_​6EC
  16.Kotsiantis, S.B.: Bagging and boosting variants for handling classifications problems: a survey. Knowl. Eng. Rev. 29(1), 78–100 (2014)CrossRef
  17.Loh, W.Y.: Classification and regression trees. Wiley Interdisc. Rev. Data Min. Knowl. Discov. 1(1), 14–23 (2011)CrossRef
  18.nVIDIA: Nvidia kepler gk110 architecture whitepaper (2014). http://​www.​nvidia.​com/​content/​PDF/​kepler/​NVIDIA-Kepler-GK110-Architecture-Whitepaper.​pdf
  19.Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A.: Classification and Regression Trees. Wadsworth International Group, California (1984)MATH
  20.Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)
  21.Reiss, A., Stricker, D.: Creating and benchmarking a new dataset for physical activity monitoring. In: The 5th Workshop on Affect and Behaviour Related Assistance (ABRA) (2012)
  22.Roggen, D., Calatroni, A., Rossi, M., Holleczek, T., Forster, K., Troster, G., Lukowicz, P., Bannach, D., Pirkl, G., Ferscha, A., Doppler, J., Holzmann, C., Kurz, M., Holl, G., Chavarriaga, R., Sagha, H., Bayati, H., Creatura, M., del R Millan, J.: Collecting complex activity datasets in highly rich networked sensor environments. In: 2010 Seventh International Conference on Networked Sensing Systems (INSS), pp. 233–240, June 2010
  23.Sagha, H., Digumarti, S.T., del R Millan, J., Chavarriaga, R., Calatroni, A., Roggen, D., Troster, G.: Benchmarking classification techniques using the opportunity human activity dataset. In: 2011 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 36–40. IEEE (2011)
  24.Ugulino, W., Cardador, D., Vega, K., Velloso, E., Milidiú, R., Fuks, H.: Wearable computing: accelerometers’ data classification of body postures and movements. In: Barros, L.N., Finger, M., Pozo, A.T., Gimenénez-Lugo, G.A., Castilho, M. (eds.) SBIA 2012. LNCS, vol. 7589, pp. 52–61. Springer, Heidelberg (2012)CrossRef
  25.Wang, Y., Huang, K., Tan, T.: Human activity recognition based on r transform. In: IEEE Conference on Computer Vision and Pattern Recognition, 2007. CVPR 2007, pp. 1–8. IEEE (2007)
  
• 作者单位：Jan Janoušek (17) (18)
  Petr Gajdoš (17) (18)
  Pavel Dohnálek (17) (18)
  Michal Radecký (17)
  
  17. Department of Computer Science, FEECS, VŠB - Technical Univesity of Ostrava, 17. listopadu 15, 708 33, Ostrava, Czech Republic
  18. IT4innovations, Centre of Excellence, VŠB - Technical Univesity of Ostrava, 17. listopadu 15, 708 33, Ostrava, Czech Republic
  
• 丛书名：Intelligent Information and Database Systems
• ISBN：978-3-662-49390-8
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

This article elaborates on the task of Human Activity Recognition being solved with the Random Forest algorithm. A performance measure is provided in terms of both recognition accuracy and computation speed. In addition, the Random Forest algorithm was implemented using CUDA, a technology providing options for massively parallel computations on low-cost hardware. The results suggest that Random Forest is a suitable and highly reliable technique for recognising human activities and that Graphics Processing Units can significantly improve the computation times of this otherwise rather time-consuming algorithm.