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Towards Detection of Human Motion

Song, Yang and Feng, Xiaolin and Perona, Pietro (2000) Towards Detection of Human Motion. In: IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Proceedings. IEEE Computer Society , Los Alamitos, CA, pp. 810-817. ISBN 0-7695-0662-3.

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Detecting humans in images is a useful application of computer vision. Loose and textured clothing, occlusion and scene clutter make it a difficult problem because bottom-up segmentation and grouping do not always work. We address the problem of detecting humans from their motion pattern in monocular image sequences; extraneous motions and occlusion may be present. We assume that we may not rely on segmentation, nor grouping and that the vision front-end is limited to observing the motion of key points and textured patches in between pairs of frames. We do not assume that we are able to track features for more than two frames. Our method is based on learning an approximate probabilistic model of the joint position and velocity of different body features. Detection is performed by hypothesis testing on the maximum a posteriori estimate of the pose and motion of the body. Our experiments on a dozen of walking sequences indicate that our algorithm is accurate and efficient.

Item Type:Book Section
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Perona, Pietro0000-0002-7583-5809
Additional Information:© 2000 IEEE. Date of Current Version: 06 August 2002. Funded by the NSF Engineering Research Center for Neuromorphic Systems Engineering (CNSE) at Caltech (NSF9402726), and by an NSF National Young Investigator Award to PP (NSF9457618). We are grateful to Luis Goncalves and Yair Weiss for helpful comments.
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Center for Neuromorphic Systems Engineering, CaltechUNSPECIFIED
Series Name:IEEE Computer Society Conference on Computer Vision and Pattern Recognition Proceedings
Record Number:CaltechAUTHORS:20111209-111812643
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Official Citation:Yang Song; Xiaolin Feng; Perona, P.; , "Towards detection of human motion," Computer Vision and Pattern Recognition, 2000. Proceedings. IEEE Conference on , vol.1, no., pp.810-817 vol.1, 2000 doi: 10.1109/CVPR.2000.855904 URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:28397
Deposited By: Ruth Sustaita
Deposited On:09 Dec 2011 19:37
Last Modified:09 Nov 2021 16:56

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