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Monocular Perception of Biological Motion - Detection and Labeling

Song, Yang and Goncalves, Luis and Di Bernardo, Enrico and Perona, Pietro (1999) Monocular Perception of Biological Motion - Detection and Labeling. In: 1999 Proceedings of the Seventh IEEE International Conference on Computer Vision. IEEE Computer Society , Los Alamitos, CA, pp. 805-812. ISBN 0-7695-0164-8.

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Computer perception of biological motion is key to developing convenient and powerful human-computer interfaces. Successful body tracking algorithms have been developed; however, initialization is done by hand. We propose a method for detecting a moving human body and for labeling its parts automatically. It is based on maximizing the joint probability density function (PDF) of the position and velocity of the body parts. The PDF is estimated from training data. Dynamic programming is used for calculating efficiently the best global labeling on an approximation of the PDF. The computational cost is on the order of N^4 where N is the number of features detected. We explore the performance of our method with experiments carried on a variety of periodic and non-periodic body motions viewed monocularly for a total of approximately 30,000 frames. Point-markers were strapped to the joints of the subject for facilitating image analysis. We find an average of 2.3% labeling error; the experiments also suggest a high degree of viewpoint-invariance.

Item Type:Book Section
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Perona, Pietro0000-0002-7583-5809
Additional Information:© 1999 IEEE. Meeting Date: 20 Sep 1999 - 27 Sep 1999; Date of Current Version: 06 August 2002.
Record Number:CaltechAUTHORS:20120111-121640630
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Official Citation:Song, Y.; Goncalves, L.; Di Bernardo, E.; Perona, P.; , "Monocular perception of biological motion-detection and labeling ," Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on , vol.2, no., pp.805-812 vol.2, 1999 doi: 10.1109/ICCV.1999.790304
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:28747
Deposited On:09 Mar 2020 14:56
Last Modified:09 Nov 2021 17:00

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