Abstract
The ability to recognize human actions in real-time is fundamental in a wide range of applications from home entertainment to medical systems. Previous work on online action recognition has shown a tradeoff between accuracy and latency. In this paper we present a novel algorithm for online action recognition that combines the discriminative power of Random Forests for feature selection and a new dynamic variation of AdaBoost for online classification.
The proposed method has been evaluated using datasets and performance metrics specifically designed for real time action recognition. Our results show that the presented algorithm is able to improve recognition rates at low latency.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ellis, C., Masood, S.Z., Tappen, M.F., Laviola Jr., J.J., Sukthankar, R.: Exploring the Trade-off Between Accuracy and Observational Latency in Action Recognition. International Journal of Computer Vision 101, 420–436 (2013)
Nowozin, S., Shotton, J.: Action Points: A Representation for Low-latency Online Human Action Recognition. Technical report (2012)
Fothergill, S., Mentis, H., Kohli, P., Nowozin, S.: Instructing people for training gestural interactive systems. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1737–1746. ACM, New York (2012)
Bloom, V., Makris, D., Argyriou, V.: G3D: A gaming action dataset and real time action recognition evaluation framework. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 7–12. IEEE (2012)
Bobick, A.F., Davis, J.W.: The recognition of human movement using temporal templates. Pattern Analysis and Machine Intelligence 23, 257–267 (2001)
Aggarwal, J.K., Ryoo, M.S.: Human activity analysis: A review. ACM Computing Surveys 43, 16:1–16:43 (2011)
Yamato, J., Ohya, J., Ishii, K.: Recognizing human action in time-sequential images using hidden Markov model. In: Computer Vision and Pattern Recognition, pp. 379–385 (1992)
Yu, E., Aggarwal, J.K.: Human action recognition with extremities as semantic posture representation. In: Computer Vision and Pattern Recognition Workshops, pp. 1–8 (2009)
Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM approach. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 3, pp. 32–36 (2004)
Dollar, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior recognition via sparse spatio-temporal features. In: Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pp. 65–72 (2005)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. The Journal of Machine Learning Research 3, 1157–1182 (2003)
Amaldi, E., Kann, V.: On the Approximability of Minimizing Nonzero Variables or Unsatisfied Relations in Linear Systems. Theoretical Computer Science 209, 237–260 (1998)
Kohavi, R., John, G.H.: Wrappers for Feature Subset Selection. Artificial intelligence 97, 273–324 (1997)
Climent-Pérez, P., Chaaraoui, A.A., Padilla-López, J.R., Flórez-Revuelta, F.: Optimal joint selection for skeletal data from RGB-D devices using a genetic algorithm. In: Batyrshin, I., Mendoza, M.G. (eds.) MICAI 2012, Part II. LNCS, vol. 7630, pp. 163–174. Springer, Heidelberg (2013)
Olshen, L., Breiman, J., Friedman, R., Stone, C.J.: Classification and Regression Trees. Wadsworth International Group (1984)
Breiman, L.: Random Forests. Machine Learning 45, 5–32 (2001)
Kinect Gesture Detection using Machine Learning, http://www.microsoft.com/en-us/download/details.aspx?id=28066
Miao, X., Heaton, J.S.: A comparison of random forest and Adaboost tree in ecosystem classification in east Mojave Desert. In: 2010 18th Int. Conf. on Geoinformatics, pp. 1–6 (2010)
Freund, Y., Schapire, R.E.: Experiments with a New Boosting Algorithm. In: International Conference on Machine Learning, pp. 148–156 (1996)
Rifkin, R., Klautau, A.: In Defense of One-Vs-All Classification. The Journal of Machine Learning Research 5, 101–141 (2004)
Friedman, J., Hastie, T., Tibshirani, R.: Additive Logistic Regression: a Statistical View of Boosting. Annals of Statistics 28, 2000 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Bloom, V., Argyriou, V., Makris, D. (2013). Dynamic Feature Selection for Online Action Recognition. In: Salah, A.A., Hung, H., Aran, O., Gunes, H. (eds) Human Behavior Understanding. HBU 2013. Lecture Notes in Computer Science, vol 8212. Springer, Cham. https://doi.org/10.1007/978-3-319-02714-2_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-02714-2_6
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02713-5
Online ISBN: 978-3-319-02714-2
eBook Packages: Computer ScienceComputer Science (R0)