The focus of this track is on automatic human multi-limb pose recovery on RGB data. We provide 9 sequences with 14 labelled limbs per person and frame, including 124,761 labelled human limbs for more than 8,000 frames. For each frame, we provide the RGB image and 14 binary masks corresponding to the 14 labelled limbs (if visible): Head, Torso, R-L Upper-arm, R-L Lower-arm, R-L Hand, R-L Upper-leg, R-L Lower-leg, and R-L Foot. For each binary mask, 1-valued pixels indicate the region in which the limb is contained.
Both competition server and the resources for participating in this Track can be found here: https://www.codalab.org/ChalearnLAP_pose
- Development phase: Create a learning system capable of learning from several training annotated human limbs a body pose recovery problem. Practice with development data (a large database of 4,000 manually labelled frames is available) and submit predictions on-line on validation data (2,000 labelled frames) to get immediate feed-back on the leaderboard. Recommended: towards the end of the development phase, submit your code for verification purpose.
- Final evaluation phase: Make predictions on the new final evaluation data (2,234 frames) revealed at the end of the development phase. The participants will have few days to train their systems and upload their predictions.
We highly recommend that the participants take advantage of this opportunity and upload regularly updated versions of their code during the development period. Their last code submission before deadline will be used for the verification.