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Mostrando entradas de mayo, 2017

Publicación en congreso: A cognitive architecture framework for critical situation awareness systems

Publicado en Lecture Notes in Computer Science Abstract Goal-oriented human-machine situation-awareness systems focus on the challenges related to perception of the elements of an environment and their state, within a time-space window, the comprehension of their meaning and the estimation of their state in the future. Present computer-supported situation awareness systems provide real-time information fusion from different sources, basic data analysis and recognition, and presentation of the corresponding data using some augmented reality principles. However, a still open research challenge is to develop advanced supervisory systems, platforms and frameworks that support higher-level cognitive activities, integrate domain specific associated knowledge, learning capabilities and decision support. To address these challenges, a novel cognitive architecture framework is presented in this paper, which emphasizes the role of the Associated Reality as a new cognitive lay

Publicación en revista: Convolutional Neural Networks and Long Short-Term Memory for skeleton-based human activity and hand gesture recognition

Publicado en Pattern Recognition Abstract In this work, we address human activity and hand gesture recognition problems using 3D data sequences obtained from full-body and hand skeletons, respectively. To this aim, we propose a deep learning-based approach for temporal 3D pose recognition problems based on a combination of a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) recurrent network. We also present a two-stage training strategy which firstly focuses on CNN training and, secondly, adjusts the full method (CNN+LSTM). Experimental testing demonstrated that our training method obtains better results than a single-stage training strategy. Additionally, we propose a data augmentation method that has also been validated experimentally. Finally, we perform an extensive experimental study on publicly available data benchmarks. The results obtained show how the proposed approach reaches state-of-the-art performance when compared to the methods i