PT Unknown
AU Sergio Escalera
   Jordi Gonzalez
   Xavier Baro
   Miguel Reyes
   Oscar Lopes
   Isabelle Guyon
   V. Athitsos
   Hugo Jair Escalante
TI Multi-modal Gesture Recognition Challenge 2013: Dataset and Results
BT 15th ACM International Conference on Multimodal Interaction
PY 2013
BP 445
EP 452
DI 10.1145/2522848.2532595
AB The recognition of continuous natural gestures is a complex and challenging problem due to the multi-modal nature of involved visual cues (e.g. fingers and lips movements, subtle facial expressions, body pose, etc.), as well as technical limitations such as spatial and temporal resolution and unreliabledepth cues. In order to promote the research advance on this field, we organized a challenge on multi-modal gesture recognition. We made available a large video database of 13; 858 gestures from a lexicon of 20 Italian gesture categories recorded with a KinectTM camera, providing the audio, skeletal model, user mask, RGB and depth images. The focus of the challenge was on user independent multiple gesture learning. There are no resting positions and the gestures are performed in continuous sequences lasting 1-2 minutes, containing between 8 and 20 gesture instances in each sequence. As a result, the dataset contains around 1:720:800 frames. In addition to the 20 main gesture categories, ‘distracter’ gestures are included, meaning that additional audioand gestures out of the vocabulary are included. The final evaluation of the challenge was defined in terms of the Levenshtein edit distance, where the goal was to indicate the real order of gestures within the sequence. 54 international teams participated in the challenge, and outstanding resultswere obtained by the first ranked participants.
ER