Contextual Rescoring for Human Pose Estimation

Antonio Hernandez-Vela, Sergio Escalera and Stan Sclaroff

In Proceedings British Machine Vision Conference 2014


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.


Poster Session


Extended Abstract (PDF, 1 page, 386K)
Paper (PDF, 11 pages, 1.7M)
Bibtex File


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.


	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 = { }