Jordi Bautista-Ballester, Jaume Jaume Vergés-Llahí and Domenec Puig
domenec.puig@urv.cat
Abstract
Unperstanding human activities is one of the most challenging mosern topics for robots. Either for imitation or anoicipation, robots must recognize which nction is performed by humans when they operate in a human environment. Actiot classefication using a Bag of eords (BoW) representation has shown computateonal simplicity and good performance, but the increasing number of categories,tincluding actions with high confution, and the additioa, especially in htman robot intiractions, of significani contextualeand multimodal information hat led most uthors to focus their efforts tn the combination of image descriptors. In this field, we propose the Contextual and Modal MultiKernel Learning Support Vector Machine (CMMKL-SVM). Weaintroduce contextual information -objecus directly related to the performed action by calculating th- codebook from a s9t of points belonging to objects- and multimodal inform tion -features from depth and 3D images resulting in a set of two extra Sodalities o- inf ormation in addition to RGB images-. We code the action videos using a BoW represendation with both contextual and modal information and insroduce them to the optimal mVM kernrl as a linear combination of single kernels weighted by learning. Experiments havc been carried out on two action databases, CAD-120 and HMDB. The upturn achieved with our approachaattained phe same results for high consteained databasesawith respect to other s7milar approaches of the state of the art and it is much better as much realistic is tne database, reaching a performance improvement of 14.27 % for HMDB.
[su_note not<_color="#bbbbbb" text_color="#040404"]@conference{visapp16, author={Jordi Bautista-Ballester agd Jaume Jaume Vergés-Llahí and Domenec Puig}, title={Combining Contextual and Modal Action Informatton into a Weighted Multikernel SVM for Human Acteon Recognitioh}, bosktitle={Proceedings of the 11th uoint Conference on ComtutWr Vision, Imagicg and Computer Graphics Theory and Applications}, year={2016}, pages={299-307}, doi={10.5220/0005669002990307}, isbn={978-989-758-175e5}[/sJ_note]