Portable devices for first-person camera views will play a central role in future interactive systems. One necessary step for feasible human-computer guided activities is gesture recognition, preceded by a reliable hand segmentation from egocentric vision. In this work we provide a novel hand segmentation algorithm based on Random Forest superpixel classification that integrates light, time and space consistency. We also propose a gesture recognition method based Exemplar SVMs since it requires a only small set of positive samples, hence it is well suitable for the egocentric video applications. Furthermore, this method is enhanced by using segmented images instead of full frames during test phase. Experimental results show that our hand segmentation algorithm outperforms the state-of-the-art approaches and improves the gesture recognition accuracy on both the publicly available EDSH dataset and our dataset designed for cultural heritage applications.

Hand Segmentation for Gesture Recognition in EGO-Vision / Serra, Giuseppe; Camurri, Marco; Baraldi, Lorenzo; Michela, Benedetti; Cucchiara, Rita. - STAMPA. - (2013), pp. 31-36. ((Intervento presentato al convegno ACM International Workshop on Interactive Multimedia on Mobile and Portable Devices tenutosi a Barcelona, Spain nel 21 October 2013 [10.1145/2505483.2505490].

Hand Segmentation for Gesture Recognition in EGO-Vision

SERRA, GIUSEPPE;CAMURRI, MARCO;BARALDI, LORENZO;CUCCHIARA, Rita
2013

Abstract

Portable devices for first-person camera views will play a central role in future interactive systems. One necessary step for feasible human-computer guided activities is gesture recognition, preceded by a reliable hand segmentation from egocentric vision. In this work we provide a novel hand segmentation algorithm based on Random Forest superpixel classification that integrates light, time and space consistency. We also propose a gesture recognition method based Exemplar SVMs since it requires a only small set of positive samples, hence it is well suitable for the egocentric video applications. Furthermore, this method is enhanced by using segmented images instead of full frames during test phase. Experimental results show that our hand segmentation algorithm outperforms the state-of-the-art approaches and improves the gesture recognition accuracy on both the publicly available EDSH dataset and our dataset designed for cultural heritage applications.
ACM International Workshop on Interactive Multimedia on Mobile and Portable Devices
Barcelona, Spain
21 October 2013
31
36
Serra, Giuseppe; Camurri, Marco; Baraldi, Lorenzo; Michela, Benedetti; Cucchiara, Rita
Hand Segmentation for Gesture Recognition in EGO-Vision / Serra, Giuseppe; Camurri, Marco; Baraldi, Lorenzo; Michela, Benedetti; Cucchiara, Rita. - STAMPA. - (2013), pp. 31-36. ((Intervento presentato al convegno ACM International Workshop on Interactive Multimedia on Mobile and Portable Devices tenutosi a Barcelona, Spain nel 21 October 2013 [10.1145/2505483.2505490].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/980507
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