Toward user-specific tracking by detection of human shapes in multi-cameras

Huang CH, Boyer E, Angonese BDC, Navab N, Ilic S (2015)

Publication Type: Conference contribution

Publication year: 2015

Journal

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition IEEE Computer Society; 1999

Publisher: IEEE Computer Society

Book Volume: 07-12-June-2015

Pages Range: 4027-4035

Conference Proceedings Title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Event location: Boston, MA, USA

ISBN: 9781467369640

DOI: 10.1109/CVPR.2015.7299029

Abstract

Human shape tracking consists in fitting a template model to temporal sequences of visual observations. It usually comprises an association step, that finds correspondences between the model and the input data, and a deformation step, that fits the model to the observations given correspondences. Most current approaches find their common ground with the Iterative-Closest-Point (ICP) algorithm, which facilitates the association step with local distance considerations. It fails when large deformations occur, and errors in the association tend to propagate over time. In this paper, we propose a discriminative alternative for the association, that leverages random forests to infer correspondences in one shot. It allows for large deformations and prevents tracking errors from accumulating. The approach is successfully integrated to a surface tracking framework that recovers human shapes and poses jointly. When combined with ICP, this discriminative association proves to yield better accuracy in registration, more stability when tracking over time, and faster convergence. Evaluations on existing datasets demonstrate the benefits with respect to the state-of-the-art.

Involved external institutions

Technische Universität München (TUM)

Germany (DE) Inria Grenoble - Rhône-Alpes research centre

France (FR)

How to cite

APA:

Huang, C.H., Boyer, E., Angonese, B.D.C., Navab, N., & Ilic, S. (2015). Toward user-specific tracking by detection of human shapes in multi-cameras. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 4027-4035). Boston, MA, USA: IEEE Computer Society.

MLA:

Huang, Chun Hao, et al. "Toward user-specific tracking by detection of human shapes in multi-cameras." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA IEEE Computer Society, 2015. 4027-4035.

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