Abstract
This paper presents a method for extracting multiple motion trajectories in human motions. We extract motion trajectories of body parts (hands and feet) using a new method based on optical flow information. This procedure is not sensitive to complicated backgrounds or color distribution of scenes. No body part model or skin color information is used in our method. We first detect Significant Motion Points (SMPs) and obtain motion trajectories by connecting related SMPs through frames using Modified Greedy Optimal Assignment (MGOA) tracker based on the distance, motion similarity, and optical flow information. We test our approach on actual ballet sequences from videos. The resulting trajectories can be used as potential features for activity recognition.
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C. J. Veeman, M.J.T. Reinders, E. Backer “Resolving Motion Correspondence for Densely Moving Points” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.23, no.1, pp. 54–72, Jan. 2001.
I.J. Cox, S.L. Hingorani “An Efficient Implementation of Reid’s Multiple Hypothesis Tracking Algorithm and Its Evaluation for the Purpose of Visual Tracking” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.18, no.2, pp. 138–150, Feb. 1996.
C. Rasmussen, G. D. Hager “Probabilistic Data Association Methods for Tracking Complex Visual Objects” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.23, no.6, pp. 560–576, June. 2001.
H.W. Kuhn “The Hungarian Method for Solving the Assignment Problem” Naval Research Logistics Quarterly, Vol.2, no.1, pp. 83–97, 1955.
M. Yang, N. Ahuja, M. Tabb. “Extraction of 2D Motion Trajectories and Its Application to Hand Gesture Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.24, no.8, pp. 1061–1074, Aug. 2002.
H. Lee, J.H. Kim “An HMM-based threshold model approach for gesture recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.21, no.10, pp. 961–973, Oct. 1999.
D. Meyer, J. Posl, H. Niemann. “Gait Classification with HMMs for Trajectories of Body Parts Extracted by Mixture Densities,” The British Machine Vision Conference, Vol.21, no.10 pp. 961–973, Oct. 1999.
J.W. Deng, H.T. Tsui “An HMM-based approach for gesture segmentation and recognition,” In Proc. International Conference on Pattern Recognition, Vol.3, pp. 679–682, 2000
J. Ben-Arie, Z. Wang, P. Pandit, S. Rajaram “Human activity recognition using multidimensional indexing,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.24, no.8, pp. 1091–1104, Aug.2002
A.F. Bobick, A.D. Wilson. “A State-Based Approach to the Representation and Recognition of Gestures,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no.12, pp. 1325–1337, Dec. 1997
V. Kwatra, A.F. Bobick, A.Y. Johnson “Temporal integration of multiple silhouette-based body-part hypotheses,” In Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 758–764, 2001
E. Polat, M. Yeasin, R. Sharma “Detecting and tracking body parts of multiple people,” In Proc. International Conference on Image Processing, vol. 1, pp. 405–408, 2001
M.J. Black, P. Anandan “The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields,” Computer Vision and Image Understanding, 63(1), pp. 75–104, Jan. 1996.
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© 2003 Springer-Verlag Berlin Heidelberg
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Min, J., Park, J.H., Kasturi, R. (2003). Extraction of Multiple Motion Trajectories in Human Motion. In: Bigun, J., Gustavsson, T. (eds) Image Analysis. SCIA 2003. Lecture Notes in Computer Science, vol 2749. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45103-X_138
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DOI: https://doi.org/10.1007/3-540-45103-X_138
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