Browse > Article
http://dx.doi.org/10.5909/JBE.2014.19.2.166

A Study for Improved Human Action Recognition using Multi-classifiers  

Kim, Semin (Dept. Information and Communications Engineering, KAIST)
Ro, Yong Man (Dept. Electrical Enginerring, KAIST)
Publication Information
Journal of Broadcast Engineering / v.19, no.2, 2014 , pp. 166-173 More about this Journal
Abstract
Recently, human action recognition have been developed for various broadcasting and video process. Since a video can consist of various scenes, keypoint approaches have been more attracted than template based methods for real application. Keypoint approahces tried to find regions having motion in video, and made 3-dimensional patches. Then, descriptors using histograms were computed from the patches, and a classifier based on machine learning method was applied to detect actions in video. However, a single classifier was difficult to handle various human actions. In order to improve this problem, approaches using multi classifiers were used to detect and to recognize objects. Thus, we propose a new human action recognition using decision-level fusion with support vector machine and sparse representation. The proposed method extracted descriptors based on keypoint approach from a video, and acquired results from each classifier for human action recognition. Then, we applied weights which were acquired by training stage to fuse each results from two classifiers. The experiment results in this paper show better result than a previous fusion method.
Keywords
Human action recognition; Multi classifiers; Decision-level fusion; Support vector machine; Sparse representation;
Citations & Related Records
연도 인용수 순위
  • Reference
1 N. Dalal, and B. Triggs, Histograms of oriented gradients for human detection, in Proc, IEEE Int. Conf. Computer Vision and Pattern Recognition, 2005, pp. 886-893.
2 P. Dollar, V. Rabaud, G. Cottrell, and S. Belongie, Behavior recognition via sparse spatio-temporal features, in Proc, IEEE Int. Work. Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 2005, pp. 65-72.
3 I. Laptev, and T. Lindeberg, Space-time interest points, in Proc, IEEE Int. Conf. Computer Vision, 2003, pp. 432-439.
4 G. Willems, T. Tuytelaars, and L. Gool, An Efficient Dense and Scale-Invariant Spatio-Temporal Interest Point Detector, in Proc, Euro. Conf. Computer Vision, 2008, pp. 650-663.
5 I. Laptev, M. Marszalek, C. Schmid, and B. Rozenfeld, Learning realistic human actions from movies, in Proc, IEEE Int. Conf. Computer Vision and Pattern Recognition, 2008, pp. 1-8.
6 A. Klaser, M. Marzalek, and C. Schmid, A spatio-temporal descriptor based on 3D-gradients, in Proc. British Machine Vision Conf., 2008, pp. 995-1004.
7 H. Wang, M.M. Ullah, A. Klaser, I. Laptev, and C. Schmid, Evaluation of local spatio-temporal features for action recognition, in Proc. British Machine Vision Conf., 2009.
8 O.L. Junior, D. Delgado, V. Goncalves, and U. Nunes, Trainable Classifier-Fusion Schemes: an Application to Pedestrian Detection, in Proc. IEEE conf. Intelligent Transportation Systems, 2009, pp. 432-437.
9 H. Liu, and S. Li, Decision fusion of sparse representation and support vector machine for SAR image target recognition, Neurocomputing Vol. 113, 2013, pp. 97-104.   DOI   ScienceOn
10 C.C. Chang, and C.J. Lin, http://www.csie.ntu.edu.tw /-cjlin/libsvm/
11 A.Y. Yang, S.S. Sastry, A. Ganesh, and YiMa, Fast $\ell$1-minimization algorithms and an application in robust face recognition: A review, in Proc. IEEE Int. Conf. Image Processing, 2010, pp.1849-1852.
12 UCF Sports, http://crcv.ucf.edu/data/UCF_Sports_Action.php