Gesture Recognition with a Convolutional Long Short-Term Memory Recurrent Neural Network

Inspired by the adequacy of convolutional neural networks in implicit extraction of visual features and the efficiency of Long Short-Term Memory Recurrent Neural Networks in dealing with long-range temporal dependencies, we propose a Convolutional Long Short-Term Memory Recurrent Neural Network (CNNLSTM) for the problem of dynamic gesture recognition. The model is able to successfully learn gestures varying in duration and complexity and proves to be a significant base for further development. Finally, the new gesture command TsironiGR-dataset for human-robot interaction is presented for the evaluation of CNNLSTM.

@InProceedings{TBW16,
 	 author =  {Tsironi, Eleni and Barros, Pablo and Wermter, Stefan},
 	 title = {Gesture Recognition with a Convolutional Long Short-Term Memory Recurrent Neural Network},
 	 booktitle = {Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)},
 	 journal = {None},
 	 editors = {}
 	 number = {}
 	 volume = {}
 	 pages = {213--218},
 	 year = {2016},
 	 month = {}
 	 publisher = {None},
 	 doi = {}
 }