Abstract
This study measures the active state of the subject body and tracks a body part or whole body using Active Quantity and color-information. The active state measures how a subject moves a body part from the movement of the subject photographed in digital video camera lively with Active Quantity between 0.0 and 1.0. When a subject stands still or when a subject disappeared, the general tracking has the problem that it is difficult to track a subject body. The proposal method can know the active state of the subject by measuring the movement of the subject with Active Quantity. And, this method can observe a subject effectively in the remote control systems such as a surveillance camera system, and the telemedicine system, because this study can estimate the active state of the subject while tracking subject body.
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References
Quaritsch, M., Kreuzthaler, M., Rinner, B., Bischof, H., Strobl, B.: Autonomous Multicamera Tracking on Embedded Smart Cameras. EURASIP Journal on Embedded Systems 2007, Article ID 92827, 10 pages (2007)
Lin, C.-W., Chang, Y.-J., Wang, C.-M., Chen, Y.-C., Sun, M.-T.: A Standard-Compliant Virtual Meeting System with Active Video Object Tracking. EURASIP Journal on Applied Signal Processing 6, 622–634 (2002)
Zotkin, D.N., Duraiswami, R., Davis, L.S.: Joint Audio-Visual Tracking Using Particle Filters. EURASIP Journal on Applied Signal Processing 11, 1154–1164 (2002)
del Blanco, C.R., Jaureguizar, F., GarcÃa, N.: Robust Tracking in Aerial Imagery Based on an Ego-Motion BayesianModel. EURASIP Journal on Advances in Signal Processing, Article ID 837405, 18 pages (2010)
Jehan-Besson, S., Barlaud, M., Aubert, G.: A 3-Step Algorithm Using Region-Based Active Contours for Video Objects Detection. EURASIP Journal on Applied Signal Processing 6, 572–581 (2002)
Pantrigo, J.J., Sánchez, A., Gianikellis, K., Montemayor, A.S.: Combining Particle Filter and Population-based Metaheuristics for Visual Articulated Motion Tracking. Electronic Letters on Computer Vision and Image Analysis 5(3), 68–83 (2005)
Funatomi, T., Iiyama, M., Kakusho, K., Minoh, M.: Distortion Correction for 3D Scan of Trunk Swaying Human Body Segments. Electronic Letters on Computer Vision and Image Analysis 7(4), 51–61 (2009)
Mukasa, T., Nobuhara, S., Maki, A., Matsuyama, T.: Finding Kinematic Structure in Time Series Volume Data. Electronic Letters on Computer Vision and Image Analysis 7(4), 62–72 (2009)
Ramasso, E., Panagiotakis, C., Rombaut, M., Pellerin, D., Tziritas, G.: Human Shape-Motion Analysis In Athletics Videos for Coarse To Fine Action/Activity Recognition Using Transferable Belief Model. Electronic Letters on Computer Vision and Image Analysis 7(4), 32–50 (2009)
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© 2012 Springer-Verlag Berlin Heidelberg
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Takai, M. (2012). Tracking of the Subject Body Using Measurement of Active Quantity and Extraction of Color-Information. In: Luo, Y. (eds) Cooperative Design, Visualization, and Engineering. CDVE 2012. Lecture Notes in Computer Science, vol 7467. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32609-7_34
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DOI: https://doi.org/10.1007/978-3-642-32609-7_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32608-0
Online ISBN: 978-3-642-32609-7
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