Abstract
In this paper, a novel human activity recognition method is proposed which utilizes independent components of activity shape information from image sequences and Hidden Markov Model (HMM) for recognition. Activities are represented by feature vectors from Independent Component Analysis (ICA) on video images and based on these features, recognition is achieved by trained HMMs of activities. Our recognition performance has been compared to the conventional method where Principle Component Analysis (PCA) is typically used to derive activity shape features. Our results show that superior recognition is achieved with our proposed method especially for activities (e.g., skipping) that cannot be easily recognized by the conventional method.
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References
Niu, F., Abdel-Mottaleb, M.: View-Invariant Human Activity Recognition Based on Shape and Motion Features. In: Proceedings of the IEEE Sixth International Symposium on Multimedia Software Engineering, pp. 546–556 (2004)
Niu, F., Abdel-Mottaleb, M.: HMM-Based Segmentation and Recognition of Human Activities from Video Sequences. In: Proceedings of IEEE International Conference on Multimedia and Expo, pp. 804–807 (2005)
Bartlett, M., Movellan, J., Sejnowski, T.: Face recognition by independent component analysis. IEEE Transactions on Neural Networks 13, 1450–1464 (2002)
Yang, J., Zhang, D., Yang, J.Y.: Is ICA Significantly Better than PCA for Face Recognition? In: Proceedings of IEEE International Conference on Computer Vision, pp. 198–203 (2005)
Lawrence, R., Rabiner, A.: Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE 77(2), 257–286 (1989)
Bregler, C., König, Y.: Eigenlips for Robust Speech Recognition. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal, Processing, pp. 669–672 (1994)
Yamato, J., Ohya, J., Ishii, K.: Recognizing Human Action in Time-Sequential Images using Hidden Markov Model. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 379–385 (1992)
Carlsson, S., Sullivan, J.: Action Recognition by Shape Matching to Key Frames. In: IEEE Computer Society Workshop on Models versus Exemplars in Computer Vision, pp. 263–270 (2002)
Cohen, I., Li, H.: Inference of Human Postures by Classification of 3D Human Body Shape. In: IEEE International Workshop on Analysis and Modeling of Faces and Gestures, pp. 74–81 (2003)
Nakata, T.: Recognizing Human Activities in Video by Multi-resolutional Optical Flow. In: International Conference on Intelligent Robots and Systems, pp. 1793–1798 (2006)
Sun, X., Chen, C., Manjunath, B.S.: Probabilistic Motion Parameter Models for Human Activity Recognition. In: 16th International Conference on Pattern recognition, pp. 443–450 (2002)
Linde, Y., Buzo, A., Gray, R.: An Algorithm for Vector Quantizer Design. IEEE Transaction on Communications 28(1), 84–94 (1980)
Iwai, Y., Hata, T., Yachida, M.: Gesture Recognition based on Subspace Method and Hidden Markov Model. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 960–966 (1997)
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Uddin, M.Z., Lee, J.J., Kim, T.S. (2008). Shape-Based Human Activity Recognition Using Independent Component Analysis and Hidden Markov Model. In: Nguyen, N.T., Borzemski, L., Grzech, A., Ali, M. (eds) New Frontiers in Applied Artificial Intelligence. IEA/AIE 2008. Lecture Notes in Computer Science(), vol 5027. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69052-8_26
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DOI: https://doi.org/10.1007/978-3-540-69052-8_26
Publisher Name: Springer, Berlin, Heidelberg
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