Abstract
The growth in interest in RGB-D devices (e.g. Microsoft Kinect or ASUS Xtion Pro) is based on their low price, as well as the wide range of possible applications. These devices can provide skeletal data consisting of 3D position, as well as orientation data, which can be further used for pose or action recognition. Data for 15 or 20 joints can be retrieved, depending on the libraries used. Recently, many datasets have been made available which allow the comparison of different action recognition approaches for diverse applications (e.g. gaming, Ambient-Assisted Living, etc.). In this work, a genetic algorithm is used to determine the contribution of each of the skeleton’s joints to the accuracy of an action recognition algorithm, thus using or ignoring the data from each joint depending on its relevance. The proposed method has been validated using a k-means-based action recognition approach and using the MSR-Action3D dataset for test. Results show the presented algorithm is able to improve the recognition rates while reducing the feature size.
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References
Johansson, G.: Visual perception of biological motion and a model for its analysis. Attention, Perception, & Psychophysics 14, 201–211 (1973), doi:10.3758/BF03212378
Polana, R., Nelson, A.: Detection and recognition of periodic, nonrigid motion. International Journal of Computer Vision 23, 261–282 (1997)
Li, W., Zhang, Z., Liu, Z.: Action recognition based on a bag of 3D points. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 9–14 (June 2010)
Wang, J., Liu, Z., Wu, Y., Yuan, J.: Mining Actionlet Ensemble for Action Recognition with Depth Cameras. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012), Providence, Rhode Island (June 2012)
Sung, J., Ponce, C., Selman, B., Saxena, A.: Human activity detection from RGBD images. In: AAAI Workshop on Pattern, Activity and Intent Recognition, PAIR (2011)
Ni, B., Wang, G., Moulin, P.: RGBD-HuDaAct: A color-depth video database for human daily activity recognition. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 1147–1153 (November 2011)
Janoch, A., Karayev, S., Jia, Y., Barron, J.T., Fritz, M., Saenko, K., Darrell, T.: A category-level 3-d object dataset: Putting the kinect to work. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 1168–1174 (November 2011)
Cantú-Paz, E.: Feature Subset Selection, Class Separability, and Genetic Algorithms. In: Deb, K., Tari, Z. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 959–970. Springer, Heidelberg (2004)
Lanzi, P.: Fast feature selection with genetic algorithms: a filter approach. In: IEEE International Conference on Evolutionary Computation, pp. 537–540 (April 1997)
Kira, K., Rendell, L.A.: The feature selection problem: traditional methods and a new algorithm. In: Proceedings of the Tenth National Conference on Artificial Intelligence, AAAI 1992, pp. 129–134. AAAI Press (1992)
Casado Yusta, S.: Different metaheuristic strategies to solve the feature selection problem. Pattern Recognition Letters 30(5), 525–534 (2009)
Yang, J., Honavar, V.: Feature subset selection using a genetic algorithm. IEEE Intelligent Systems and their Applications 13(2), 44–49 (1998)
Raptis, M., Kirovski, D., Hoppe, H.: Real-Time Classification of Dance Gestures from Skeleton Animation. In: Proceedings of the 10th Annual ACM SIGGRAPH/Eurographics Symposium on Computer Animation, SCA 2011, pp. 147–156 (2011)
John, G., Kohavi, R., Pfleger, K.: Irrelevant features and the subset selection problem. In: Proceedings of the 11th International Conference on Machine Learning, pp. 121–129. Morgan Kaufmann, San Francisco (1994)
Liu, H., Motoda, H.: Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, Boston (1998)
Cheema, S., Eweiwi, A., Thurau, C., Bauckhage, C.: Action recognition by learning discriminative key poses. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 1302–1309 (November 2011)
Chaaraoui, A.A., Climent-Pérez, P., Flórez-Revuelta, F.: An Efficient Approach for Multi-view Human Action Recognition Based on Bag-of-Key-Poses. In: Salah, A.A., Ruiz-del-Solar, J., Meriçli, Ç., Oudeyer, P.-Y. (eds.) HBU 2012. LNCS, vol. 7559, pp. 29–40. Springer, Heidelberg (2012)
Yang, X., Tian, Y.: EigenJoints-based Action Recognition Using Naïve-Bayes-Nearest-Neighbor. In: Second International Workshop on Human Activity Understanding from 3D Data in Conjunction with CVPR, 2012, Providence, Rhode Island (2012)
Punch, W., Goodman, E., Pei, M., Chia-Shun, L., Hovland, P., Enbody, R.: Further research on feature selection and classification using genetic algorithms. In: Proceedings of the 5th International Conference on Genetic Algorithms, pp. 557–564 (1993)
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Climent-Pérez, P., Chaaraoui, A.A., Padilla-López, J.R., Flórez-Revuelta, F. (2013). Optimal Joint Selection for Skeletal Data from RGB-D Devices Using a Genetic Algorithm. In: Batyrshin, I., Mendoza, M.G. (eds) Advances in Computational Intelligence. MICAI 2012. Lecture Notes in Computer Science(), vol 7630. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37798-3_15
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DOI: https://doi.org/10.1007/978-3-642-37798-3_15
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