2016 |
I. Rodomagoulakis, N. Kardaris, V. Pitsikalis, A. Arvanitakis, P. Maragos A multimedia gesture dataset for human robot communication: Acquisition, tools and recognition results Conference Proceedings - International Conference on Image Processing, ICIP, 2016-August , 2016, ISSN: 15224880. Abstract | BibTeX | Links: [PDF] @conference{334, title = {A multimedia gesture dataset for human robot communication: Acquisition, tools and recognition results}, author = { I. Rodomagoulakis and N. Kardaris and V. Pitsikalis and A. Arvanitakis and P. Maragos}, url = {http://robotics.ntua.gr/wp-content/uploads/publications/RKPAM_MultimedaGestureDataset-HRI_ICIP2016.pdf}, doi = {10.1109/ICIP.2016.7532923}, issn = {15224880}, year = {2016}, date = {2016-01-01}, booktitle = {Proceedings - International Conference on Image Processing, ICIP}, volume = {2016-August}, pages = {3066--3070}, abstract = {Motivated by the recent advances in human-robot interaction we present a new dataset, a suite of tools to handle it and state-of-the-art work on visual gestures and audio commands recognition. The dataset has been collected with an integrated annotation and acquisition web-interface that facilitates on-the-way temporal ground-truths for fast acquisition. The dataset includes gesture instances in which the subjects are not in strict setup positions, and contains multiple scenarios, not restricted to a single static configuration. We accompany it by a valuable suite of tools as the practical interface to acquire audio-visual data in the robotic operating system, a state-of-the-art learning pipeline to train visual gesture and audio command models, and an online gesture recognition system. Finally, we include a rich evaluation of the dataset providing rich and insightfull experimental recognition results.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Motivated by the recent advances in human-robot interaction we present a new dataset, a suite of tools to handle it and state-of-the-art work on visual gestures and audio commands recognition. The dataset has been collected with an integrated annotation and acquisition web-interface that facilitates on-the-way temporal ground-truths for fast acquisition. The dataset includes gesture instances in which the subjects are not in strict setup positions, and contains multiple scenarios, not restricted to a single static configuration. We accompany it by a valuable suite of tools as the practical interface to acquire audio-visual data in the robotic operating system, a state-of-the-art learning pipeline to train visual gesture and audio command models, and an online gesture recognition system. Finally, we include a rich evaluation of the dataset providing rich and insightfull experimental recognition results. |
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