Abstract
The identification of human activity in video, for example whether a person is walking, clapping, waving, etc. is extremely important for video interpretation. In this paper we present a systematic approach to extracting visual features from image sequences that are used for classifying different activities. Furthermore, since different people perform the same action across different number of frames, matching training and test sequences is not a trivial task. We discuss a new technique for video shot matching where the shots matched are of different sizes. The proposed technique is based on frequency domain analysis of feature data and it is shown to achieve very high accuracy of 94.5% on recognizing a number of different human actions.
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References
Ayers, D., Shah, M.: Monitoring Human Behavior from Video Taken in an Office Environment. Image and Vision Computing 19, 833–846 (2001)
Bobick, A.F., Ivanov, Y.A.: Action Recognition using Probabilistic Parsing. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 196–202 (1998)
Bobick, A.F., Davis, J.W.: The Recognition of Human Movement using Temporal Templates. IEEE Transactions on PAMI 23(3), 257–267 (2001)
Duda, R., Hart, P.E., Stork, D.: Pattern Classification, 2nd edn. John Wiley and Sons, New York (2001)
Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast Subsequence Matching in Time-Series Databases. In: Proceedings ACM SIGMOD Conference, Mineapolis, MN, pp. 419–429 (1994)
Hongeng, S., Nevatia, R., Bremond, F.: Video-based Event Recognition: Activity Representation and Probabilistic Recognition Methods. Computer Vision and Image Understanding 96, 129–162 (2004)
Huang, P.S., Haris, C.J., Nixon, M.S.: Human Gait Recognition in Canonical Space using Temporal Templates. In: IEEE Proceedings—Vision, Image and Signal processing, vol. 146, pp. 93–100 (1999)
Kim, S.H., Park, R.-H.: An Efficient Algorithm For Video Sequence Matching Using The Modified Hausdorff Distance and the Directed Divergence. IEEE Transactions on Circuits and Systems for Video Technology 12, 296–592 (2002)
Liang, R.-H., Ouhyoung, M.: A Real-Time Continuous Gesture Recognition System for Sign Language. In: Proc. Int. Conference on Automatic Face and Gesture Recognition, Nara, Japan, pp. 558–565 (1998)
Little, J., Boyd, J.: Recognizing People by Their Gait: the Shape of Motion. VIDERE 1(2) (1998)
Masoud, O., Papanikolopoulos, N.: A Method for Human Action Recognition. Image and Vision Computing 21, 729–743 (2003)
Ou, J., Chen, X., Yang, J.: Gesture Recognition for Remote Collaborative Physical Tasks using Tablet PCs. In: ICCV Workshop on Multimedia Tech. in E-Learning and Collaboration (2003)
Ozki, M., Nakamura, Y., Ohta, Y.: Human Behavior Recognition for an Intelligent Video Production System. In: IEEE Pacific Rim Conference on Multimedia, pp. 1153–1160 (2002)
Ramamoorthy, A., Vaswani, N., Chaudhury, S., Banerjee, S.: Recognition of Dynamic Hand Gestures. Pattern Recognition 36, 2069–2081 (2003)
Rangarajan, K., Allen, B., Shah, M.: Matching Motion Trajectories. Pattern Recognition 26, 595–610 (1993)
Rao, C., Yilmaze, A., Shah, M.: View-Invariant Representation and Recognition of Actions. International Journal of Computer vision 50(2), 203–226 (2002)
Schüldt, C., Laptev, I., Caputo, B.: Recognizing Human Actions: A Local SVM Approach. ICPR. 3, 32–36 (2004)
Stark, M., Kohler, M., Zyklop, P.G.: Video Based Gesture Recognition for Human Computer Interaction. Informationstechnik und Technische Informatik 38(3), 15–20 (1996)
Starner, T., Pentland, A.: Visual Recognition of American Sign Language using Hidden Markov Models. In: Proc. Int. Workshop on Automatic Face and Gesture Recognition (1995)
Stolcke, A.: Bayesian Learning of Probabilistic Language Models, Phd., University of California at Berkeley (1994)
Tolba, A.: Arabic Glove: A Communication Aid for the Vocally Impaired. Pattern Analysis and Applications 1(4), 218–230 (1998)
Tsai, P.-S., Shah, M., Keiter, K., Kasparis, T.: Cyclic Motion Detection, Computer Science Technical Report, University of Central Florida, Orlando (1993)
Wang, J., Singh, S.: Video Based Human Dynamics: A Review. Real Time Imaging 9(5), 321–346 (2003)
Wang, J.: “Video Based Understanding of Human Dynamics: A Machine Learning Approach, PhD Thesis, Exeter Univ (2005)
Zobl, M., Wallhoff, F., Rigoll, G.: Action Recognition in Meeting Scenarios using Global Motion Features. In: Ferryman, J. (ed.) Proceedings Fourth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, March 2003, pp. 32–36. University of Reading, Graz, Osterreich (2003)
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Singh, S., Wang, J. (2006). Human Activity Recognition in Videos: A Systematic Approach. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_31
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DOI: https://doi.org/10.1007/11875581_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45485-4
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