Contextual Rescoring for Human Pose Estimation
In Proceedings British Machine Vision Conference 2014
http://dx.doi.org/10.5244/C.28.121
Abstract
A contextual rescoring method is proposed for improving the detection of body joints of a pictorial structure model for human pose estimation. A set of mid-level parts is incorporated in the model, and their detections are used to extract spatial and score-related features relative to other body joint hypotheses. A technique is proposed for the automatic discovery of a compact subset of poselets that covers a set of validation images while maximizing precision. A rescoring mechanism is defined as a set-based boosting classifier that computes a new score for body joint detections, given its relationship to detections of other body joints and mid-level parts in the image. This new score complements the unary potential of a discriminatively trained pictorial structure model. Experiments on two benchmarks show performance improvements when considering the proposed mid-level image representation and rescoring approach in comparison with other pictorial structure-based approaches.
Session
Poster Session
Files
Extended Abstract (PDF, 1 page, 386K)Paper (PDF, 11 pages, 1.7M)
Bibtex File
Citation
Antonio Hernandez-Vela, Sergio Escalera, and Stan Sclaroff. Contextual Rescoring for Human Pose Estimation. Proceedings of the British Machine Vision Conference. BMVA Press, September 2014.
BibTex
@inproceedings{BMVC.28.121
title = {Contextual Rescoring for Human Pose Estimation},
author = {Hernandez-Vela, Antonio and Escalera, Sergio and Sclaroff, Stan},
year = {2014},
booktitle = {Proceedings of the British Machine Vision Conference},
publisher = {BMVA Press},
editors = {Valstar, Michel and French, Andrew and Pridmore, Tony}
doi = { http://dx.doi.org/10.5244/C.28.121 }
}