Desarrollo de un sistema para la interpretación y predicción de la situación del tráfico mediante Deep Learning

La comprensión semántica de una escena es un aspecto clave en múltiples aplicaciones de inteligencia artificial, tanto para los Sistemas Inteligentes de Transporte como para los robots. En este Trabajo Fin de Máster se diseña, desarrolla y evalúa un sistema que, basado en la segmentación semántica de imágenes, obtenida mediante una red neuronal convolucional, permite realizar las distintas tareas que abarca la comprensión de una escena: clasificación, detección de objetos y la propia segmentación semántica, de una manera sencilla y eficiente. Además, proponemos una solución enfocada a vehículos inteligentes, que permite, utilizando la segmentación semántica, estimar la velocidad a la que debe circular el vehículo. Para ello, hemos construido una nueva base de datos en la que poder evaluar este nuevo problema. Los resultados confirman que es posible y beneficioso confiar en la segmentación semántica para llevar a cabo las distintas tareas.Semantic scene understanding is a key aspect of multiple artificial intelligence applications, from Intelligent Transportation Systems to robotics. In this Final Project, we design, develop and evaluate a system that, based on the semantic segmentation of images obtained through a convolutional neural network, allows to carry out the different tasks comprising scene understanding: classification, object detection and the aforementioned semantic segmentation, in a simple yet efficient manner. In addition, we propose a solution focused on intelligent vehicles, which allows us, using semantic segmentation, to estimate the speed at which the vehicle must be driven. To this end, we have built a new database in which we can evaluate this challenging new problem. The results confirm that it is possible and beneficial to rely on semantic segmentation to successfully perform the different tasks.Máster Universitario en Ingeniería de Telecomunicación (M125

Boosting Multi-Vehicle Tracking with a Joint Object Detection and Viewpoint Estimation Sensor

In this work, we address the problem of multi-vehicle detection and tracking for traffic monitoring applications. We preset a novel intelligent visual sensor for tracking-by-detection with simultaneous pose estimation. Essentially, we adapt an Extended Kalman Filter (EKF) to work not only with the detections of the vehicles but also with their estimated coarse viewpoints, directly obtained with the vision sensor. We show that enhancing the tracking with observations of the vehicle pose, results in a better estimation of the vehicles trajectories. For the simultaneous object detection and viewpoint estimation task, we present and evaluate two independent solutions. One is based on a fast GPU implementation of a Histogram of Oriented Gradients (HOG) detector with Support Vector Machines (SVMs). For the second, we adequately modify and train the Faster R-CNN deep learning model, in order to recover from it not only the object localization but also an estimation of its pose. Finally, we publicly release a challenging dataset, the GRAM Road Traffic Monitoring (GRAM-RTM), which has been especially designed for evaluating multi-vehicle tracking approaches within the context of traffic monitoring applications. It comprises more than 700 unique vehicles annotated across more than 40.300 frames of three videos. We expect the GRAM-RTM becomes a benchmark in vehicle detection and tracking, providing the computer vision and intelligent transportation systems communities with a standard set of images, annotations and evaluation procedures for multi-vehicle tracking. We present a thorough experimental evaluation of our approaches with the GRAM-RTM, which will be useful for establishing further comparisons. The results obtained confirm that the simultaneous integration of vehicle localizations and pose estimations as observations in an EKF, improves the tracking results