Learning visual representations with deep neural networks for intelligent transportation systems problems

Esta tesis se centra en dos grandes problemas en el área de los sistemas de transportes inteligentes (STI): el conteo de vehículos en escenas de congestión de tráfico; y la detección y estimación del punto de vista, de forma simultánea, de los objetos en una escena. Respecto al problema del conteo, este trabajo se centra primero en el diseño de arquitecturas de redes neuronales profundas que tengan la capacidad de aprender representaciones multi-escala profundas, capaces de estimar de forma precisa la cuenta de objetos, mediante mapas de densidad. Se trata también el problema de la escala de los objetos introducida por la gran perspectiva típicamente presente en el área de recuento de objetos. Además, con el éxito de las redes hourglass profundas en el campo del conteo de objetos, este trabajo propone un nuevo tipo de red hourglass profunda con conexiones de corto circuito auto-gestionadas. Los modelos propuestos se evalúan en las bases de datos públicas más utilizadas y logran los resultados iguales o superiores al estado del arte en el momento en que fueron publicadas. Para la segunda parte, se realiza un estudio comparativo completo del problema de detección de objetos y la estimación de la pose de forma simultánea. Se expone el compromiso existente entre la localización del objeto y la estimación de su pose. Un detector necesita idealmente una representación que sea invariable al punto de vista, mientras que un estimador de poses necesita ser discriminatorio. Por lo tanto, se proponen tres nuevas arquitecturas de redes neurales profundas en las que el problema de la detección de objetos y la estimación de la pose se van desacoplando progresivamente. Además, se aborda la cuestión de si la pose debe expresarse como un valor discreto o continuo. A pesar de ofrecer un rendimiento similar, los resultados muestran que los enfoques continuos son más sensibles al sesgo del punto de vista principal de la categoría del objeto. Se realiza un análisis comparativo detallado en las dos bases de datos principales, es decir, PASCAL3D+ y ObjectNet3D. Se logran resultados competitivos con todos los modelos propuestos en ambos conjuntos de datos

Wayfinder:Towards Automatically Deriving Optimal OS Configurations

Tuning operating systems configuration in order to obtain the maximum application performance is a hard problem. This is due to the extremely large size of the configuration space offered by modern OSes, and to the fact that it is generally explored manually. To address that issue, we propose to bring automation to the OS configuration space exploration process, in order to derive effortlessly and as quickly as possible optimal OS configurations for a given use case. We present Wayfinder, a generic OS performance evaluation platform. Wayfinder is fully automated and ensures both the accuracy and reproducibility of results, all the while speeding up how fast tests are run on a system. Wayfinder is easily extensible and offers convenient APIs to (1) implement custom configuration space exploration techniques, (2) add new benchmarks and (3) support additional OS projects. We demonstrate Wayfinder’s capacity to automatically and efficiently explore a LibOS’ networking configuration space; as well as its ability to efficiently isolate parallel experiments to avoid noisy neighbors

Learning to Transfer with von Neumann Conditional Divergence

The similarity of feature representations plays a pivotal role in the success of problems related to domain adaptation. Feature similarity includes both the invariance of marginal distributions and the closeness of conditional distributions given the desired response y (e.g., class labels). Unfortunately, traditional methods always learn such features without fully taking into consideration the information in y, which in turn may lead to a mismatch of the conditional distributions or the mixup of discriminative structures underlying data distributions. In this work, we introduce the recently proposed von Neumann conditional divergence to improve the transferability across multiple domains. We show that this new divergence is differentiable and eligible to easily quantify the functional dependence between features and y. Given multiple source tasks, we integrate this divergence to capture discriminative information in y and design novel learning objectives assuming those source tasks are observed either simultaneously or sequentially. In both scenarios, we obtain favorable performance against state-of-the-art methods in terms of smaller generalization error on new tasks and less catastrophic forgetting on source tasks (in the sequential setup)

Fast Reciprocal Nearest Neighbors Clustering

Abstract This paper presents a novel approach for accelerating the popular Reciprocal Nearest Neighbors (RNN) clustering algorithm, i.e. the fast-RNN. We speed up the nearest neighbor chains construction via a novel dynamic slicing strategy for the projection search paradigm. We detail an efficient implementation of the clustering algorithm along with a novel data structure, and present extensive experimental results that illustrate the excellent performance of fast-RNN in low-and high-dimensional spaces. A C++ implementation has been made publicly available

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

Leer en comunidad III: creación y desarrollo de clubes de lectura de literatura escrita por mujeres en la universidad

Depto. de Lengua Española y Teoría de la LiteraturaFac. de FilologíaFALSEsubmitte