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ChaLearn Looking at People Challenge 2014: Dataset and Results

Escalera, S; Baro, X; Gonzalez, J; Bautista, MA; Madadi, M; Reyes, M; Ponce-Lopez, V; ... Guyon, I; + view all (2015) ChaLearn Looking at People Challenge 2014: Dataset and Results. In: Agapito, L and Bronstein, MM and Rother, C, (eds.) European Conference on Computer Vision ECCV 2014: Computer Vision - ECCV 2014 Workshops. (pp. pp. 459-473). Springer, Cham Green open access

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Abstract

This paper summarizes the ChaLearn Looking at People 2014 challenge data and the results obtained by the participants. The competition was split into three independent tracks: human pose recovery from RGB data, action and interaction recognition from RGB data sequences, and multi-modal gesture recognition from RGB-Depth sequences. For all the tracks, the goal was to perform user-independent recognition in sequences of continuous images using the overlapping Jaccard index as the evaluation measure. In this edition of the ChaLearn challenge, two large novel data sets were made publicly available and the Microsoft Codalab platform were used to manage the competition. Outstanding results were achieved in the three challenge tracks, with accuracy results of 0.20, 0.50, and 0.85 for pose recovery, action/interaction recognition, and multi-modal gesture recognition, respectively.

Type: Proceedings paper
Title: ChaLearn Looking at People Challenge 2014: Dataset and Results
Event: 13th European Conference on Computer Vision (ECCV)
Location: Zurich, SWITZERLAND
Dates: 06 September 2014 - 12 September 2014
ISBN-13: 978-3-319-16177-8
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-319-16178-5_32
Publisher version: https://doi.org/10.1007/978-3-319-16178-5_32
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Human pose recovery, Behavior analysis, Action and interactions, Multi-modal gestures, Recognition
UCL classification: UCL
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URI: https://discovery.ucl.ac.uk/id/eprint/10114952
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