Seguimiento de múltiples objetos en entornos interiores muy poblados basado en la combinación de métodos probabilísticos y determinísticos

La presente tesis se encuentra enmarcada dentro del área de la robótica personal y de servicios. Es éste un área de investigación que ha tomado gran relevancia en las últimas dos décadas gracias a los continuos avances de la tecnología y su inserción en la vida diaria de la sociedad moderna. Dentro de este contexto, en la tesis se propone un nuevo algoritmo para el seguimiento de múltiples objetos ("multiple target tracking", MTT abreviadamente), concebido para su uso en entornos interiores complejos. El proceso de MTT diseñado, proporciona información completa sobre los diferentes objetos detectados en cada momento en el entorno del robot, indicando el número, posición, velocidad, camino recorrido e identidad de los mismos. Esta información es obtenida por el algoritmo de seguimiento a partir de los datos recogidos por el sistema de observación de entrada al sistema. La solución propuesta cumple todas las especificaciones establecidas por el comportamiento deseado para el seguidor: ha de tener en cuenta la incertidumbre de los modelos de estado y medida de los objetos bajo seguimiento; ha de ser flexible al uso de distintos tipos de sensores ha de poder adaptarse al tipo de información de entrada al algoritmo que proporcione el sistema (visión, ultrasonidos, infrarrojo, radio frecuencia, etc.) que conformen el sistema de observación empleado; debe ser capaz de seguir los diferentes tipos de objetos que el robot pueda encontrar en su movimiento por el entorno, independientemente de la dinámica o la forma de estos objetos; finalmente, tiene que alcanzar el nivel de robustez y fiabilidad que requiere la aplicación de robótica personal en la que se enmarca, en la cual la seguridad del propio robot y de los objetos seguidos (generalmente personas u otros robots) es una especificación básica. Para poder cumplir todas las especificaciones necesarias, el algoritmo de seguimiento diseñado en esta tesis adopta como mejor solución la combinación de métodos probabilísticos y determinísticos. De este modo, se propone un filtro de partículas como núcleo de estimación del algoritmo de seguimiento, al cual se le incorporan dos procesos de clasificación que actúan, respectivamente, como algoritmo de asociación y filtro de salida. Esta combinación da lugar al "Filtro de Partículas Extendido con Proceso de Clasificación" ("Extended Particle Filter with Clustering Process", XPFCP), nombre con el que se identifica el algoritmo propuesto por la autora para el seguimiento de múltiples objetos en entornos interiores muy poblados. El filtro de partículas permite modelar múltiples estados en una única distribución multimodal; su flexibilidad lo hace idóneo para su aplicación con distintos tipos de modelos de estado y observación. Tales características convierten a esta versión del filtro de Bayes en la más adecuada para realizar el seguimiento de múltiples objetos, con la prestación adicional de poder realizar tal tarea de seguimiento con un coste computacional prácticamente constante. La idea de usar el filtro de partículas como estimador multimodal en aplicaciones de seguimiento ya ha sido propuesta en varios trabajos previos de investigación, pero la falta de robustez del sistema así obtenido ha llevado en todos los casos a descartar estas soluciones. Esta tesis propone y demuestra que la incorporación de una parte determinística en el algoritmo de seguimiento basado en filtro de partículas añade la robustez que el estimador multimodal requiere. En el presente documento, se incluye una profunda revisión (sobre algoritmos y resultados) de los trabajos llevados a cabo por la comunidad científica en esta misma línea de investigación. Además, se muestra también un estudio exhaustivo del comportamiento del sistema de seguimiento propuesto en situaciones complejas en términos de robustez, fiabilidad, eficiencia y tiempo de ejecución. Finalmente, se realiza la comparación de la solución diseñada por la autora con dos de los algoritmos más conocidos y usados por la comunidad científica en tareas de seguimiento similares: el "Filtro de Asociación Conjunta de Datos" o "Joint Probabilistic Data Association Filter", en su versión continua (JPDAF) y muestreada (SJPDAF). Estas comparativas permiten contrastar y validar la contribución de la presente tesis en esta área de investigación

A new framework for deep learning video based Human Action Recognition on the edge

Nowadays, video surveillance systems are commonly found in most public and private spaces. These systems typically consist of a network of cameras that feed into a central node. However, the processing aspect is evolving towards distributed approaches, leveraging edge-computing. These distributed systems are capable of effectively addressing the detection of people or events at each individual node. Most of these systems, rely on the use of deep-learning and segmentation algorithms which enable them to achieve high performance, but usually with a significant computational cost, hindering real-time execution. This paper presents an approach for people detection and action recognition in the wild, optimized for running on the edge, and that is able to work in real-time, in an embedded platform. Human Action Recognition (HAR) is performed by using a Recurrent Neural Network (RNN), specifically a Long Short-Term Memory (LSTM). The input to the LSTM is an ad-hoc, lightweight feature vector obtained from the bounding box of each detected person in the video surveillance image. The resulting system is highly portable and easily scalable, providing a powerful tool for real-world video surveillance applications (in the wild and real-time action recognition). The proposal has been exhaustively evaluated and compared against other state-of-the-art (SOTA) proposals in five datasets, including four widely used (KTH, WEIZMAN, WVU, IXMAX) and a novel one (GBA) recorded in the wild, that includes several people performing different actions simultaneously. The obtained results validate the proposal, since it achieves SOTA accuracy within a much more complicated video surveillance real scenario, and using a lightweight embedded hardware.European CommissionAgencia Estatal de InvestigaciónUniversidad de Alcal

Remote control of a robotic unit: a case study for control engineering formation

Hands-on experimentation has widely demonstrated its efficacy in engineering training, especially in control formation, since experimentation using computer-aided control system design (CACSD) tools is essential for future engineers. In this context, this article describes a case study for Control Engineering formation, based on a new lab practice for the linear and angular velocity control for a commercial P3-DX robot platform, to teach industrial control. This lab proposal includes all the stages involved in the design of a real control system, from plant identification from an open-loop test to real experimentation of the designed control system. The lab practices proposed have a twofold objective: First, it is an interdisciplinary approach that allows students to put into practice the skills from other subjects in the curriculum, facilitating the integration of knowledge. In addition, it allows increasing the motivation of the students by working with a complex and realistic plant. The proposal has been evaluated through the grades of the students, as well as the perception of both students and instructors, and the results obtained allow to confirm the benefits of the proposal.Universidad de Alcal

Effect of Carbamazepine, Ibuprofen, Triclosan and Sulfamethoxazole on Anaerobic Bioreactor Performance: Combining Cell Damage, Ecotoxicity and Chemical Information

Pharmaceuticals and personal care products (PPCPs) are partially degraded in wastewater treatment plants (WWTPs), thereby leading to the formation of more toxic metabolites. Bacterial populations in bioreactors operated in WWTPs are sensitive to different toxics such as heavy metals and aromatic compounds, but there is still little information on the effect that pharmaceuticals exert on their metabolism, especially under anaerobic conditions. This work evaluated the effect of selected pharmaceuticals that remain in solution and attached to biosolids on the metabolism of anaerobic biomass. Batch reactors operated in parallel under the pressure of four individual and mixed PPCPs (carbamazepine, ibuprofen, triclosan and sulfametoxazole) allowed us to obtain relevant information on anaerobic digestion performance, toxicological effects and alterations to key enzymes involved in the biodegradation process. Cell viability was quantitatively evaluated using an automatic analysis of confocal microscopy images, and showed that triclosan and mixed pollutants caused higher toxicity and cell death than the other individual compounds. Both individual pollutants and their mixture had a considerable impact on the anaerobic digestion process, favoring carbon dioxide production, lowering organic matter removal and methane production, which also produced microbial stress and irreversible cell damage.Comunidad de MadridUniversidad de Alcal

Towards Deep People Detection using CNNs Trained on Synthetic Images

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3DFCNN: real-time action recognition using 3D deep neural networks with raw depth information

This work describes an end-to-end approach for real-time human action recognition from raw depth image-sequences. The proposal is based on a 3D fully convolutional neural network, named 3DFCNN, which automatically encodes spatio-temporal patterns from raw depth sequences. The described 3D-CNN allows actions classification from the spatial and temporal encoded information of depth sequences. The use of depth data ensures that action recognition is carried out protecting people"s privacy, since their identities can not be recognized from these data. The proposed 3DFCNN has been optimized to reach a good performance in terms of accuracy while working in real-time. Then, it has been evaluated and compared with other state-of-the-art systems in three widely used public datasets with different characteristics, demonstrating that 3DFCNN outperforms all the non-DNNbased state-of-the-art methods with a maximum accuracy of 83.6% and obtains results that are comparable to the DNN-based approaches, while maintaining a much lower computational cost of 1.09 seconds, what significantly increases its applicability in real-world environments.Agencia Estatal de InvestigaciónUniversidad de Alcal

Odometry and Laser Scanner Fusion Based on a Discrete Extended Kalman Filter for Robotic Platooning Guidance

This paper describes a relative localization system used to achieve the navigation of a convoy of robotic units in indoor environments. This positioning system is carried out fusing two sensorial sources: (a) an odometric system and (b) a laser scanner together with artificial landmarks located on top of the units. The laser source allows one to compensate the cumulative error inherent to dead-reckoning; whereas the odometry source provides less pose uncertainty in short trajectories. A discrete Extended Kalman Filter, customized for this application, is used in order to accomplish this aim under real time constraints. Different experimental results with a convoy of Pioneer P3-DX units tracking non-linear trajectories are shown. The paper shows that a simple setup based on low cost laser range systems and robot built-in odometry sensors is able to give a high degree of robustness and accuracy to the relative localization problem of convoy units for indoor applications

Stereo Vision Tracking of Multiple Objects in Complex Indoor Environments

This paper presents a novel system capable of solving the problem of tracking multiple targets in a crowded, complex and dynamic indoor environment, like those typical of mobile robot applications. The proposed solution is based on a stereo vision set in the acquisition step and a probabilistic algorithm in the obstacles position estimation process. The system obtains 3D position and speed information related to each object in the robot’s environment; then it achieves a classification between building elements (ceiling, walls, columns and so on) and the rest of items in robot surroundings. All objects in robot surroundings, both dynamic and static, are considered to be obstacles but the structure of the environment itself. A combination of a Bayesian algorithm and a deterministic clustering process is used in order to obtain a multimodal representation of speed and position of detected obstacles. Performance of the final system has been tested against state of the art proposals; test results validate the authors’ proposal. The designed algorithms and procedures provide a solution to those applications where similar multimodal data structures are found

Comparing Improved Versions of 'K-Means' and 'Subtractive' Clustering in a Tracking Application

Abstract. A partitional and a fuzzy clustering algorithm are compared in this paper in terms of accuracy, robustness and efficiency. 3D position data extracted from a stereo-vision system have to be clustered to use them in a tracking application in which a particle filter is the kernel of the estimation task. 'K-Means' and 'Subtractive' algorithms have been modified and enriched with a validation process in order improve its functionality in the tracking system. Comparisons and conclusions of the clustering results both in a stand-alone process and in the proposed tracking task are shown in the paper

Effect of Carbamazepine, Ibuprofen, Triclosan and Sulfamethoxazole on Anaerobic Bioreactor Performance: Combining Cell Damage, Ecotoxicity and Chemical Information

Pharmaceuticals and personal care products (PPCPs) are partially degraded in wastewater treatment plants (WWTPs), thereby leading to the formation of more toxic metabolites. Bacterial populations in bioreactors operated in WWTPs are sensitive to different toxics such as heavy metals and aromatic compounds, but there is still little information on the effect that pharmaceuticals exert on their metabolism, especially under anaerobic conditions. This work evaluated the effect of selected pharmaceuticals that remain in solution and attached to biosolids on the metabolism of anaerobic biomass. Batch reactors operated in parallel under the pressure of four individual and mixed PPCPs (carbamazepine, ibuprofen, triclosan and sulfametoxazole) allowed us to obtain relevant information on anaerobic digestion performance, toxicological effects and alterations to key enzymes involved in the biodegradation process. Cell viability was quantitatively evaluated using an automatic analysis of confocal microscopy images, and showed that triclosan and mixed pollutants caused higher toxicity and cell death than the other individual compounds. Both individual pollutants and their mixture had a considerable impact on the anaerobic digestion process, favoring carbon dioxide production, lowering organic matter removal and methane production, which also produced microbial stress and irreversible cell damage