2016 |
A Guler, N Kardaris, S Chandra, V Pitsikalis, C Werner, K Hauer, C Tzafestas, P Maragos, I Kokkinos Human Joint Angle Estimation and Gesture Recognition for Assistive Robotic Vision Conference Proc. of Workshop on Assistive Computer Vision and Robotics, European Conf. on Computer Vision (ECCV-2016), Amsterdam, The Netherlands, 2016. Abstract | BibTeX | Links: [PDF] @conference{guler_joint_gesture_2016, title = {Human Joint Angle Estimation and Gesture Recognition for Assistive Robotic Vision}, author = {A Guler and N Kardaris and S Chandra and V Pitsikalis and C Werner and K Hauer and C Tzafestas and P Maragos and I Kokkinos}, url = {http://robotics.ntua.gr/wp-content/publications/PoseEstimGestureRecogn-AssistRobotVision_ACVR2016-ECCV-Workshop.pdf}, doi = {10.1007/978-3-319-48881-3_29}, year = {2016}, date = {2016-10-01}, booktitle = {Proc. of Workshop on Assistive Computer Vision and Robotics, European Conf. on Computer Vision (ECCV-2016)}, address = {Amsterdam, The Netherlands}, abstract = {We explore new directions for automatic human gesture recognition and human joint angle estimation as applied for human-robot interaction in the context of an actual challenging task of assistive living for real-life elderly subjects. Our contributions include state-of-the-art approaches for both low- and mid-level vision, as well as for higher level action and gesture recognition. The first direction investigates a deep learning based framework for the challenging task of human joint angle estimation on noisy real world RGB-D images. The second direction includes the employment of dense trajectory features for online processing of videos for automatic gesture recognition with real-time performance. Our approaches are evaluated both qualitative and quantitatively on a newly acquired dataset that is constructed on a challenging real-life scenario on assistive living for elderly subjects.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } We explore new directions for automatic human gesture recognition and human joint angle estimation as applied for human-robot interaction in the context of an actual challenging task of assistive living for real-life elderly subjects. Our contributions include state-of-the-art approaches for both low- and mid-level vision, as well as for higher level action and gesture recognition. The first direction investigates a deep learning based framework for the challenging task of human joint angle estimation on noisy real world RGB-D images. The second direction includes the employment of dense trajectory features for online processing of videos for automatic gesture recognition with real-time performance. Our approaches are evaluated both qualitative and quantitatively on a newly acquired dataset that is constructed on a challenging real-life scenario on assistive living for elderly subjects. |
2008 |
George Caridakis, Olga Diamanti, Kostas Karpouzis, Petros Maragos Automatic sign language recognition Conference Proceedings of the 1st ACM international conference on PErvasive Technologies Related to Assistive Environments - PETRA '08, 2008, ISBN: 9781605580678. Abstract | BibTeX | Links: [PDF] @conference{208, title = {Automatic sign language recognition}, author = { George Caridakis and Olga Diamanti and Kostas Karpouzis and Petros Maragos}, url = {http://portal.acm.org/citation.cfm?doid=1389586.1389687}, doi = {10.1145/1389586.1389687}, isbn = {9781605580678}, year = {2008}, date = {2008-01-01}, booktitle = {Proceedings of the 1st ACM international conference on PErvasive Technologies Related to Assistive Environments - PETRA '08}, pages = {1}, abstract = {This work focuses on two of the research problems comprising automatic sign language recognition, namely robust computer vision techniques for consistent hand detection and tracking, while preserving the hand shape contour which is useful for extraction of features related to the handshape and a novel classification scheme incorporating Self-organizing maps, Markov chains and Hidden Markov Models. Geodesic Active Contours enhanced with skin color and motion information are employed for the hand detection and the extraction of the hand silhouette, while features extracted describe hand trajectory, region and shape. Extracted features are used as input to separate classifiers, forming a robust and adaptive architecture whose main contribution is the optimal utilization of the neighboring characteristic of the SOM during the decoding stage of the Markov chain, representing the sign class.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } This work focuses on two of the research problems comprising automatic sign language recognition, namely robust computer vision techniques for consistent hand detection and tracking, while preserving the hand shape contour which is useful for extraction of features related to the handshape and a novel classification scheme incorporating Self-organizing maps, Markov chains and Hidden Markov Models. Geodesic Active Contours enhanced with skin color and motion information are employed for the hand detection and the extraction of the hand silhouette, while features extracted describe hand trajectory, region and shape. Extracted features are used as input to separate classifiers, forming a robust and adaptive architecture whose main contribution is the optimal utilization of the neighboring characteristic of the SOM during the decoding stage of the Markov chain, representing the sign class. |
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