NEWS: The tentative program with the accepted papers can be found here. Registration at the ECCV conference already covers Workshop papers.

NEWS: The labels of the final evaluation set have been published. We have added the scripts and labels for each track on the Test data tab of the corresponding track at the data download server. You need the team credentials to access this information.

NEWS: You can check the results of the Chalearn Looking at People 2014 here. Congratulations to all the teams that have participated, especially to the winners, and we hope you had enjoyed the challenge!

ChaLearn organizes in 2014 three parallel challenge tracks on Human Pose Recovery on RGB data, action/interaction spotting on RGB data, and gesture spotting on RGB-Depth data.

The challenge features three quantitative tracts:

Track 1: Human Pose Recovery: More than 8,000 frames of continuous RGB sequences are recorded and labeled with the objective of performing human pose recovery by means of recognizing more than 120,000 human limbs of different people. Examples of labeled frames are shown in Fig. 1.

Track 2: Action/Interaction Recognition: 235 performances of 11 action/interaction categories are recorded and manually labeled in continuous RGB sequences of different people performing natural isolated and collaborative actions randomly. Examples of labeled actions are shown in Fig. 1.

Track 3: Gesture Recognition: More than 14,000 gestures are drawn from a vocabulary of 20 Italian sign gesture categories. The emphasis of this third track is on multi-modal automatic learning of a set of 20 gestures performed by several different users, with the aim of performing user independent continuous gesture spotting. An example of the visual modalities for each frame of the data set is shown in Fig.2.

Top three ranked participants on each track will be awarded and invited to follow the ECCV workshop submission guide for inclusion of a description of their system at the ECCV proceedings and submit an extended paper in a special issue on gesture recognition at a Special Issue of the TPAMI journal.