Abstract
The KinectTMcamera has revolutionized the field of computer vision by making available low cost 3D cameras recording both RGB and depth data, using a structured light infrared sensor. We recorded and made available a large database of 50,000 hand and arm gestures. With these data, we organized a challenge emphasizing the problem of learning from very few examples. 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, Marshalling 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 subtasks related to, but different from the tasks on which the competitors are tested.
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Guyon, I., Athitsos, V., Jangyodsuk, P., Escalante, H.J., Hamner, B. (2013). Results and Analysis of the ChaLearn Gesture Challenge 2012. In: Jiang, X., Bellon, O.R.P., Goldgof, D., Oishi, T. (eds) Advances in Depth Image Analysis and Applications. WDIA 2012. Lecture Notes in Computer Science, vol 7854. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40303-3_19
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DOI: https://doi.org/10.1007/978-3-642-40303-3_19
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