Towards robots reasoning about group behavior of museum visitors: leader detection and group tracking

The final publication is available at IOS Press through http://dx.doi.org/10.3233/AIS-170467Peer ReviewedPostprint (author's final draft

Multivariate Regression with Incremental Learning of Gaussian Mixture Models

La publicació definitiva d'aquest treball està disponible a IOS Press a través de http://dx.doi.org/10.3233/978-1-61499-806-8-196Within the machine learning framework, incremental learning of multivariate spaces is of special interest for on-line applications. In this work, the regression problem for multivariate systems is solved by implementing an efficient probabilistic incremental algorithm. It allows learning high-dimensional redundant non-linear maps by the cumulative acquisition of data from input-output systems. The proposed model is aimed at solving prediction and inference problems. The implementation introduced in this work allows learning from data batches without the need of keeping them in memory afterwards. The learning architecture is built using Incremental Gaussian Mixture Models. The Expectation-Maximization algorithm and general geometric properties of Gaussian distributions are used to train the models. Our current implementation can produce accurate results fitting models in real multivariate systems. Results are shown from testing the algorithm for both situations, one where the incremental learning is demonstrated and the second where the performance to solve the regression problem is evaluated on a toy example.Peer ReviewedPostprint (author's final draft

Active Appearance Models for People Recognition with a RGB-D Sensor

Active Appearance Models (AAMs) is a computer vision procedure for statistical matching of object shape and appearance between images. The present work will analyze how AAMs can be employed with RGB-Depth technology, so robots endowing a Kinect sensor can perceive and recognize humans more effectively in order to unequivocally identify them. This research is associated to the RoCKIn@Home challenge, an initiative from the European RoCKIn project focusing on domestic service robots

2D and 3D automatic landmarking of body contours for people recognition

Automatic landmarking has overcome a main drawback in Active Appearance Models (AAMs) computer vision technique: landmarks must be manually placed on training images to construct the shape mesh. Although AAMs are a well-known procedure for statistical matching of object shape and texture between images, hand-landmarking makes it very time consuming and not automatically applicable on new objects observed in images. There is a vast body of work applying automatic landmarking on faces or body joints for AAM training and several other purposes. In this paper, first we explore the possibility to extend one of these methods to full body contours on still images supplied by a single camera and demonstrate it is a plausible approach in terms of accuracy and speed measures from experimentation. Then, a 3D upgrade approach is presented using a RGB-D sensor to detect and automatically landmark body shapes with high accuracy in real-time, as a comparison with the latest technology. Our proposal represents a new research line in human body pose tracking with a single-view camera and the first steps of a novel contribution in learning body appearance. Hence, further implementation in robots would lead to people being recognized and identified by them with any vision resources, no matter how primitive or Advanced in human-robot interaction tasks.Peer ReviewedPostprint (published version

The life history patterns of brachyuran crabs

SIGLEAvailable from British Library Document Supply Centre- DSC:DX173580 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Single-camera automatic landmarking for people recognition with an ensemble of regression trees

Active Appearance Model (AAM) is a computer vision procedure for statistical matching of object shape and appearance between images. A main drawback in this technique comes from the construction of the shape mesh. Since landmarks must be manually placed when training shapes, AAM is a very time consuming procedure and it cannot be automatically applied on new objects observed in the images. An approach for automatic landmarking of body shapes on still images for AAM training is introduced in this paper. Several works exist applying automatic landmarking on faces or body joints. Here, we explore the possibility to extend one of these methods to full body contours and demonstrate it is a plausible approach in terms of accuracy and speed measures in experimentation. Our proposal represents a new research line in human body pose tracking with a single-view camera. Hence, implementation in real-time would lead to people being recognized by robots endowed with minimal vision resources, like a webcam, in human-robot interaction tasks.Peer Reviewe

2D and 3D automatic landmarking of body contours for people recognition

Automatic landmarking has overcome a main drawback in Active Appearance Models (AAMs) computer vision technique: landmarks must be manually placed on training images to construct the shape mesh. Although AAMs are a well-known procedure for statistical matching of object shape and texture between images, hand-landmarking makes it very time consuming and not automatically applicable on new objects observed in images. There is a vast body of work applying automatic landmarking on faces or body joints for AAM training and several other purposes. In this paper, first we explore the possibility to extend one of these methods to full body contours on still images supplied by a single camera and demonstrate it is a plausible approach in terms of accuracy and speed measures from experimentation. Then, a 3D upgrade approach is presented using a RGB-D sensor to detect and automatically landmark body shapes with high accuracy in real-time, as a comparison with the latest technology. Our proposal represents a new research line in human body pose tracking with a single-view camera and the first steps of a novel contribution in learning body appearance. Hence, further implementation in robots would lead to people being recognized and identified by them with any vision resources, no matter how primitive or Advanced in human-robot interaction tasks.Peer Reviewe

Towards robots reasoning about group behavior of museum visitors: leader detection and group tracking

The final publication is available at IOS Press through http://dx.doi.org/10.3233/AIS-170467Peer Reviewe

Multivariate Regression with Incremental Learning of Gaussian Mixture Models

La publicació definitiva d'aquest treball està disponible a IOS Press a través de http://dx.doi.org/10.3233/978-1-61499-806-8-196Within the machine learning framework, incremental learning of multivariate spaces is of special interest for on-line applications. In this work, the regression problem for multivariate systems is solved by implementing an efficient probabilistic incremental algorithm. It allows learning high-dimensional redundant non-linear maps by the cumulative acquisition of data from input-output systems. The proposed model is aimed at solving prediction and inference problems. The implementation introduced in this work allows learning from data batches without the need of keeping them in memory afterwards. The learning architecture is built using Incremental Gaussian Mixture Models. The Expectation-Maximization algorithm and general geometric properties of Gaussian distributions are used to train the models. Our current implementation can produce accurate results fitting models in real multivariate systems. Results are shown from testing the algorithm for both situations, one where the incremental learning is demonstrated and the second where the performance to solve the regression problem is evaluated on a toy example.Peer Reviewe

The role of somatosensory models in vocal autonomous exploration

The present work focuses on two main objectives. Firstly, it highlights the relevance of studying the early stages of language development using machines as an approach to contribute to the future of speech recognizers and synthesizers, user interfaces, active learning techniques, and to the field of robotics and artificial intelligence in general. Secondly, this work introduces some results on the study of the role of somatosensory models in vocal autonomous exploration. In previous works, the roles of intrinsic motivations and motor constraints in early vocal development were studied showing that active learning techniques can be used by artificial agents endowed with a simulated vocal tract to autonomously learn how to produce intended sounds through the use of probabilistic models. This work studies the effects of modifying the somatosensory model, which is used to map motor commands to undesired articulatory configurations, over the intrinsically motivated active learning process. The somatosensory system is modeled as a Gaussian Mixture Model. Herein, some simulations were run varying the structure of the model in order to analyze differences in the results. The effects on the explored sensorimotor regions and the amount of undesired vocal configurations are studied. The simulations presented in this work show that the structure of the current somatosensory model is relevant to the learning process. However, it can be also concluded that in order to reliably characterize the effects of modifying the somatosensory model further simulations must be performed and clear measures for performance should be considered. // El trabajo presentado persigue dos objetivos principales: el primero de ellos es mostrar la necesidad de estudiar las etapas tempranas del desarrollo del lenguaje utilizando máquinas. Estos estudios contribuirán en el desarrollo futuro de sintetizadores y reconocedores de voz, interfaces de usuario e indirectamente al estudio de la inteligencia artificial; el segundo objetivo es presentar nuevos resultados en el estudio sobre el rol de los sistemas somatosensores en la exploración vocal temprana. En trabajos preliminares fueron estudiados los roles de las motivaciones intrínsecas y las restricciones motoras en el desarrollo vocal temprano. De estos estudios se concluyó que las técnicas de aprendizaje automático activo pueden ser utilizadas en conjunto con agentes artificiales dotados con un tracto vocal simulado para aprender autónomamente cómo producir sonidos específicos. En el presente trabajo se estudian los efectos del cambio de los parámetros que definen el modelo probabilístico del sistema somatosensorial, el cual mapea configuraciones motoras con configuraciones articulares indeseadas sobre el proceso de aprendizaje. El sistema somatosensorial es modelado utilizando “Gaussian Mixture Models”. A través del resultado de una serie de simulaciones donde se modifica la estructura del modelo antes mencionado, se demuestra que la estructura del modelo somatosensorial es relevante para el proceso de aprendizaje. Sin embargo, los resultados también indican que para realizar una mejor caracterización de los efectos de la modificación del modelo somatosensorial deben llevarse a cabo más simulaciones, así como tomar en consideración nuevas medidas de calidad del aprendizaje.Peer ReviewedPostprint (published version