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Author |
Sergio Escalera; Jordi Gonzalez; Xavier Baro; Miguel Reyes; Oscar Lopes; Isabelle Guyon; V. Athitsos; Hugo Jair Escalante |
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Title |
Multi-modal Gesture Recognition Challenge 2013: Dataset and Results |
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Conference Article |
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Year |
2013 |
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15th ACM International Conference on Multimodal Interaction |
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445-452 |
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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 unreliable
depth 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 audio
and 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 results
were obtained by the first ranked participants. |
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Sidney; Australia; December 2013 |
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978-1-4503-2129-7 |
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ICMI |
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Notes |
HUPBA; ISE; 600.063;MV |
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no |
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Admin @ si @ EGB2013 |
Serial |
2373 |
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Author |
Victor Ponce; Sergio Escalera; Xavier Baro |
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Title |
Multi-modal Social Signal Analysis for Predicting Agreement in Conversation Settings |
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Conference Article |
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Year |
2013 |
Publication |
15th ACM International Conference on Multimodal Interaction |
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495-502 |
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In this paper we present a non-invasive ambient intelligence framework for the analysis of non-verbal communication applied to conversational settings. In particular, we apply feature extraction techniques to multi-modal audio-RGB-depth data. We compute a set of behavioral indicators that define communicative cues coming from the fields of psychology and observational methodology. We test our methodology over data captured in victim-offender mediation scenarios. Using different state-of-the-art classification approaches, our system achieve upon 75% of recognition predicting agreement among the parts involved in the conversations, using as ground truth the experts opinions. |
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Sidney; Australia; December 2013 |
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978-1-4503-2129-7 |
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ICMI |
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HuPBA;MV |
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no |
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Call Number |
Admin @ si @ PEB2013 |
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2488 |
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