Paper Info
Title
Chalearn Looking At People Challenge 2014: Dataset And Results
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. Out-standing 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.
Year
DOI
Venue
2014
10.1007/978-3-319-16178-5_32
COMPUTER VISION - ECCV 2014 WORKSHOPS, PT I
Keywords
Field
DocType
Human pose recovery, Behavior analysis, Action and interactions, Multi-modal gestures, Recognition
Computer vision,Data set,Computer science,Gesture recognition,RGB color model,Data sequences,Artificial intelligence,Jaccard index,Machine learning
Conference
Volume
ISSN
Citations 
8925
0302-9743
62
PageRank 
References 
Authors
1.74
15
10
Name
Order
Citations
PageRank
Sergio Escalera11415113.31
Xavier Baró247433.99
Jordi Gonzalez361748.02
Miguel Ángel Bautista416810.97
Meysam Madadi5879.28
Miguel Reyes626410.08
Víctor Ponce-López71327.10
Hugo Jair Escalante893973.89
Jamie Shotton97571324.72
Isabelle Guyon10110331544.34