Human shape and pose tracking using keyframes

Huang CH, Boyer E, Navab N, Ilic S (2014)

Publication Type: Conference contribution

Publication year: 2014

Journal

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

Publisher: IEEE Computer Society

Pages Range: 3446-3453

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

Event location: Columbus, OH, USA

ISBN: 9781479951178

DOI: 10.1109/CVPR.2014.440

Abstract

This paper considers human tracking in multi-view setups and investigates a robust strategy that learns online key poses to drive a shape tracking method. The interest arises in realistic dynamic scenes where occlusions or segmentation errors occur. The corrupted observations present missing data and outliers that deteriorate tracking results. We propose to use key poses of the tracked person as multiple reference models. In contrast to many existing approaches that rely on a single reference model, multiple templates represent a larger variability of human poses. They provide therefore better initial hypotheses when tracking with noisy data. Our approach identifies these reference models online as distinctive keyframes during tracking. The most suitable one is then chosen as the reference at each frame. In addition, taking advantage of the proximity between successive frames, an efficient outlier handling technique is proposed to prevent from associating the model to irrelevant outliers. The two strategies are successfully experimented with a surface deformation framework that recovers both the pose and the shape. Evaluations on existing datasets also demonstrate their benefits with respect to the state of the art.

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) Inria Grenoble - Rhône-Alpes research centre FR France (FR)

How to cite

APA:

Huang, C.-H., Boyer, E., Navab, N., & Ilic, S. (2014). Human shape and pose tracking using keyframes. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 3446-3453). Columbus, OH, USA: IEEE Computer Society.

MLA:

Huang, Chun-Hao, et al. "Human shape and pose tracking using keyframes." Proceedings of the 27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, OH, USA IEEE Computer Society, 2014. 3446-3453.

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