ChaLearn Looking at People Challenge 2014: Dataset and Results [chapter]

Sergio Escalera, Xavier Baró, Jordi Gonzàlez, Miguel A. Bautista, Meysam Madadi, Miguel Reyes, Víctor Ponce-López, Hugo J. Escalante, Jamie Shotton, Isabelle Guyon
2015 Lecture Notes in Computer Science  
This paper summarizes the ChaLearn Looking at People 2014 challenge. 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
more » ... large novel datasets were made publicly available and the Microsoft Codalab platform were used to manage the competition. Results achieved an overlapping accuracy about 0.20 and 0.50 for pose recovery and action/interaction spotting, showing still much margin for improvement, meanwhile an overlapping about 0.85 was achieved for multi-modal gesture recognition, making it feasible to be applied in real applications.
doi:10.1007/978-3-319-16178-5_32 fatcat:arlegvujt5c2fb3fmou4fx5iwu