ChaLearn gesture challenge: Design and first results

Abstract

We organized a challenge on gesture recognition: http://gesture.chalearn.org. We made available a large database of 50,000 hand and arm gestures videorecorded with a Kinect™ camera providing both RGB and depth images. We used the Kaggle platform to automate submissions and entry evaluation. The focus of the challenge is on “one-shot-learning”, which means training gesture classifiers from a single video clip example of each gesture. The data are split into subtasks, each using a small vocabulary of 8 to 12 gestures, related to a particular application domain: hand signals used by divers, finger codes to represent numerals, signals used by referees, marchalling signals to guide vehicles or aircrafts, etc. We limited the problem to single users for each task and to the recognition of short sequences of gestures punctuated by returning the hands to a resting position. This situation is encountered in computer interface applications, including robotics, education, and gaming. The challenge setting fosters progress in transfer learning by providing for training a large number of sub-tasks related to, but different from the tasks on which the competitors are tested.