An enhanced method for human action recognition

Journal of Advanced Research - Tập 6 - Trang 163-169 - 2015
Mona M. Moussa1, Elsayed Hamayed2, Magda B. Fayek2, Heba A. El Nemr1
1Computers and Systems Department, Electronics Research Institute, Egypt
2Computer Engineering Department, Faculty of Engineering, Cairo University, Egypt

Tài liệu tham khảo

Fathi, 2008, Action recognition by learning mid-level motion features, Comput Vision Pattern Recogn, CVPR IEEE, 1 Blank, 2005, Actions as space-time shapes, Int Conf Comput Vision, ICCV IEEE, 2, 1395 Ke, 2005, Efficient visual event detection using volumetric features, Int Conf Comput Vision, ICCV IEEE, 1, 166 Sheikh, 2005, Exploring the space of a human action, Int Conf Comput Vision, ICCV IEEE, 144 Chen MY, Hauptmann AG. MoSIFT: recognizing human actions in surveillance videos. Technological report, CMU-CS-09-161, Carnegie Mellon University; 2009. p. 9–161. Schuldt, 2004, Recognizing human actions: a local SVM approach, Int Conf Pattern Recogn, ICPR IEEE, 3, 32 Csurka, 2004, Visual categorization with bags of key points, ECCV International Workshop on Statistical Learning in Computer Vision, 1 Gemert, 2008, Kernel code-books for scene categorization, Proc Euro Conf Comput Vision, ECCV, 696 Lowe, 2004, Distinctive image features from scale-invariant keypoints, Int J Comput Vision, 60, 91, 10.1023/B:VISI.0000029664.99615.94 Lin Z, Jiang Z, Davis LS. Recognizing actions by shape-motion prototype trees. Int Conf Comput Vision, ICCV IEEE. p. 1–8. Liu, 2008, Learning human actions via information maximization, Comput Vision Pattern Recogn, CVPR IEEE, 1 Bregonzio, 2012, Fusing appearance and distribution information of interest points for action recognition, Pattern Recogn, 45, 1220, 10.1016/j.patcog.2011.08.014 Niebles, 2008, Unsupervised learning of human action categories using spatial-temporal words, Int J Comput Vision, 79, 299, 10.1007/s11263-007-0122-4 Sadanand, 2012, Action bank: a high-level representation of activity in video, Comput Vision Pattern Recogn, CVPR IEEE, 1234 Tran, 2011, Modeling motion of body parts for action recognition, British Mach Vision Conf, BMVC Kovashka, 2010, Learning a hierarchy of discriminative space-time neighborhood features for human action recognition, Comput Vision Pattern Recogn, CVPR IEEE, 2046 Vedaldi A, Fulkerson B. VLFeat. An open and portable library of computer vision algorithms; 2008. <http://www.vlfeat.org/>. Lai, 2010, Human action recognition using key points displacement, Int Conf Image Signal Process, ICISP, 6134, 439, 10.1007/978-3-642-13681-8_51 MacQueen, 1967, Some methods for classification and analysis of multivariate observations, Proc 5th Berkeley symposium on mathematical statistics and probability, 1, 281 Wang, 2012, Comparative study of encoding, pooling and normalization methods for action recognition, Asian Conf Comput Vision, ACCV, 7726, 572 Jayalakshmi, 2011, Statistical normalization and back propogation for classification, Int J Comput Theor Eng (IJCTE), 3, 89, 10.7763/IJCTE.2011.V3.288 Chang, 2011, LIBSVM: a library for support vector machines, ACM Trans Intell Syst Technol, TIST, 2, 1, 10.1145/1961189.1961199 Gao Z, Chen MY, Hauptmann AG, Cai A. Comparing evaluation protocols on the KTH dataset. In: International conference on human behavior understanding, vol. 6219, Springer; 2010. p. 88–100. Cao, 2010, Cross-dataset action detection, Comput Vision Pattern Recogn, CVPR IEEE, 1998 Kaaniche, 2010, Gesture recognition by learning local motion signatures, Comput Vision Pattern Recogn, CVPR IEEE, 2745 Dollar, 2005, Behavior recognition via sparse spatio-temporal features, IEEE international workshop on visual surveillance and performance evaluation of tracking and surveillance, 65, 10.1109/VSPETS.2005.1570899 Klaser, 2008, A spatio-temporal descriptor based on 3D-gradients, British Mach Vision Conf, BMVC, 995 Zhang, 2008, Motion context: a new representation for human action recognition, Proceedings of the European conference on computer vision, ECCV Springer, 5305, 817