Segmentación semántica con modelos de deep learning y etiquetados no densos

La segmentación semántica es un problema muy estudiado dentro del campo de la visión por computador que consiste en la clasificación de imágenes a nivel de píxel. Es decir, asignar una etiqueta o valor a cada uno de los píxeles de la imagen. Tiene aplicaciones muy variadas, que van desde interpretar el contenido de escenas urbanas para tareas de conducción automática hasta aplicaciones médicas que ayuden al médico a analizar la información del paciente para realizar un diagnóstico o operaciones. Como en muchos otros problemas y tareas relacionados con la visión por computador, en los últimos años se han propuesto y demostrado grandes avances en los métodos para segmentación semántica gracias, en gran parte, al reciente auge de los métodos basados en aprendizaje profundo o deep learning.\\ A pesar de que en los últimos años se están realizando mejoras constantes, los modelos de \textit{deep learning} para segmentación semántica %así como otras áreas, tienen un problema presentan un reto que dificulta su aplicabilidad a problemas de la vida real: necesitan grandes cantidades de anotaciones para entrenar los modelos. Esto es muy costoso, sobre todo porque en este caso hay que realizarlo a nivel de píxel. Muchos conjuntos de datos reales, por ejemplo datos adquiridos para tareas de monitorización del medio ambiente (grabaciones de entornos naturales, imágenes de satélite) generalmente presentan tan solo unos pocos píxeles etiquetados por imagen, que suelen venir de algunos clicks de un experto, para indicar ciertas zonas de interés en esas imágenes. Este tipo de etiquetado hace %imposible que sea muy complicado el entrenamiento de modelos densos que permitan procesar y obtener de manera automática una mayor cantidad de información de todos estos conjuntos de datos.\\ El objetivo de este trabajo es proponer nuevos métodos para resolver este problema. La idea principal es utilizar una segmentación inicial de la imagen multi-nivel de la imagen para propagar la poca información disponible. Este enfoque novedoso permite aumentar la anotación, y demostramos que pese a ser algo ruidosa, permite aprender de manera efectiva un modelo que obtenga la segmentación deseada. Este método es aplicable a cualquier tipo de dispersión de las anotaciones, siendo independiente del número de píxeles anotados. Las principales tareas desarrolladas en este proyecto son: -Estudio del estado del arte en técnicas de segmentación semántica (la mayoría basadas en técnicas de deep learning) -Propuesta y evaluación de métodos para aumentar (propagar) las etiquetas de las imágenes de entrenamiento cuando estas son dispersas y escasas -Diseño y evaluación de las arquitecturas de redes neuronales más adecuadas para resolver este problema Para validar nuestras propuestas, nos centramos en un caso de aplicación en imágenes submarinas, capturadas para monitorización de las zonas de barreras de coral. También demostramos que el método propuesto se puede aplicar a otro tipo de imágenes, como imágenes aéreas, imágenes multiespectrales y conjuntos de datos de segmentación de instancias

TractorEYE: Vision-based Real-time Detection for Autonomous Vehicles in Agriculture

Agricultural vehicles such as tractors and harvesters have for decades been able to navigate automatically and more efficiently using commercially available products such as auto-steering and tractor-guidance systems. However, a human operator is still required inside the vehicle to ensure the safety of vehicle and especially surroundings such as humans and animals. To get fully autonomous vehicles certified for farming, computer vision algorithms and sensor technologies must detect obstacles with equivalent or better than human-level performance. Furthermore, detections must run in real-time to allow vehicles to actuate and avoid collision.This thesis proposes a detection system (TractorEYE), a dataset (FieldSAFE), and procedures to fuse information from multiple sensor technologies to improve detection of obstacles and to generate a map. TractorEYE is a multi-sensor detection system for autonomous vehicles in agriculture. The multi-sensor system consists of three hardware synchronized and registered sensors (stereo camera, thermal camera and multi-beam lidar) mounted on/in a ruggedized and water-resistant casing. Algorithms have been developed to run a total of six detection algorithms (four for rgb camera, one for thermal camera and one for a Multi-beam lidar) and fuse detection information in a common format using either 3D positions or Inverse Sensor Models. A GPU powered computational platform is able to run detection algorithms online. For the rgb camera, a deep learning algorithm is proposed DeepAnomaly to perform real-time anomaly detection of distant, heavy occluded and unknown obstacles in agriculture. DeepAnomaly is -- compared to a state-of-the-art object detector Faster R-CNN -- for an agricultural use-case able to detect humans better and at longer ranges (45-90m) using a smaller memory footprint and 7.3-times faster processing. Low memory footprint and fast processing makes DeepAnomaly suitable for real-time applications running on an embedded GPU. FieldSAFE is a multi-modal dataset for detection of static and moving obstacles in agriculture. The dataset includes synchronized recordings from a rgb camera, stereo camera, thermal camera, 360-degree camera, lidar and radar. Precise localization and pose is provided using IMU and GPS. Ground truth of static and moving obstacles (humans, mannequin dolls, barrels, buildings, vehicles, and vegetation) are available as an annotated orthophoto and GPS coordinates for moving obstacles. Detection information from multiple detection algorithms and sensors are fused into a map using Inverse Sensor Models and occupancy grid maps. This thesis presented many scientific contribution and state-of-the-art within perception for autonomous tractors; this includes a dataset, sensor platform, detection algorithms and procedures to perform multi-sensor fusion. Furthermore, important engineering contributions to autonomous farming vehicles are presented such as easily applicable, open-source software packages and algorithms that have been demonstrated in an end-to-end real-time detection system. The contributions of this thesis have demonstrated, addressed and solved critical issues to utilize camera-based perception systems that are essential to make autonomous vehicles in agriculture a reality

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

A computer vision system for detecting and analysing critical events in cities

Whether for commuting or leisure, cycling is a growing transport mode in many cities worldwide. However, it is still perceived as a dangerous activity. Although serious incidents related to cycling leading to major injuries are rare, the fear of getting hit or falling hinders the expansion of cycling as a major transport mode. Indeed, it has been shown that focusing on serious injuries only touches the tip of the iceberg. Near miss data can provide much more information about potential problems and how to avoid risky situations that may lead to serious incidents. Unfortunately, there is a gap in the knowledge in identifying and analysing near misses. This hinders drawing statistically significant conclusions to provide measures for the built-environment that ensure a safer environment for people on bikes. In this research, we develop a method to detect and analyse near misses and their risk factors using artificial intelligence. This is accomplished by analysing video streams linked to near miss incidents within a novel framework relying on deep learning and computer vision. This framework automatically detects near misses and extracts their risk factors from video streams before analysing their statistical significance. It also provides practical solutions implemented in a camera with embedded AI (URBAN-i Box) and a cloud-based service (URBAN-i Cloud) to tackle the stated issue in the real-world settings for use by researchers, policy-makers, or citizens. The research aims to provide human-centred evidence that may enable policy-makers and planners to provide a safer built environment for cycling in London, or elsewhere. More broadly, this research aims to contribute to the scientific literature with the theoretical and empirical foundations of a computer vision system that can be utilised for detecting and analysing other critical events in a complex environment. Such a system can be applied to a wide range of events, such as traffic incidents, crime or overcrowding

Semantics and statistics for automated image annotation

Automated image annotation consists of a number of techniques that aim to find the correlation between words and image features such as colour, shape, and texture to provide correct annotation words to images. In particular, approaches based on Bayesian theory use machine-learning techniques to learn statistical models from a training set of pre-annotated images and apply them to generate annotations for unseen images. The focus of this thesis lies in demonstrating that an approach, which goes beyond learning the statistical correlation between words and visual features and also exploits information about the actual semantics of the words used in the annotation process, is able to improve the performance of probabilistic annotation systems. Specifically, I present three experiments. Firstly, I introduce a novel approach that automatically refines the annotation words generated by a non-parametric density estimation model using semantic relatedness measures. Initially, I consider semantic measures based on co-occurrence of words in the training set. However, this approach can exhibit limitations, as its performance depends on the quality and coverage provided by the training data. For this reason, I devise an alternative solution that combines semantic measures based on knowledge sources, such as WordNet and Wikipedia, with word co-occurrence in the training set and on the web, to achieve statistically significant results over the baseline. Secondly, I investigate the effect of using semantic measures inside an evaluation measure that computes the performance of an automated image annotation system, whose annotation words adopt the hierarchical structure of an ontology. This is the case of the ImageCLEF2009 collection. Finally, I propose a Markov Random Field that exploits the semantic context dependencies of the image. The best result obtains a mean average precision of 0.32, which is consistent with the state-of-the-art in automated image annotation for the Corel 5k dataset. </br

Intent prediction of vulnerable road users for trusted autonomous vehicles

This study investigated how future autonomous vehicles could be further trusted by vulnerable road users (such as pedestrians and cyclists) that they would be interacting with in urban traffic environments. It focused on understanding the behaviours of such road users on a deeper level by predicting their future intentions based solely on vehicle-based sensors and AI techniques. The findings showed that personal/body language attributes of vulnerable road users besides their past motion trajectories and physics attributes in the environment led to more accurate predictions about their intended actions

Highly efficient low-level feature extraction for video representation and retrieval.

PhDWitnessing the omnipresence of digital video media, the research community has raised the question of its meaningful use and management. Stored in immense multimedia databases, digital videos need to be retrieved and structured in an intelligent way, relying on the content and the rich semantics involved. Current Content Based Video Indexing and Retrieval systems face the problem of the semantic gap between the simplicity of the available visual features and the richness of user semantics. This work focuses on the issues of efficiency and scalability in video indexing and retrieval to facilitate a video representation model capable of semantic annotation. A highly efficient algorithm for temporal analysis and key-frame extraction is developed. It is based on the prediction information extracted directly from the compressed domain features and the robust scalable analysis in the temporal domain. Furthermore, a hierarchical quantisation of the colour features in the descriptor space is presented. Derived from the extracted set of low-level features, a video representation model that enables semantic annotation and contextual genre classification is designed. Results demonstrate the efficiency and robustness of the temporal analysis algorithm that runs in real time maintaining the high precision and recall of the detection task. Adaptive key-frame extraction and summarisation achieve a good overview of the visual content, while the colour quantisation algorithm efficiently creates hierarchical set of descriptors. Finally, the video representation model, supported by the genre classification algorithm, achieves excellent results in an automatic annotation system by linking the video clips with a limited lexicon of related keywords

Visual Storytelling: Captioning of Image Sequences

In the space of automated captioning, the task of visual storytelling is a dimension. Given sequences of images as inputs, visual storytelling (VIST) is about automatically generating textual narratives as outputs. Automatically producing stories for an order of pictures or video frames have several potential applications in diverse domains ranging from multimedia consumption to autonomous systems. The task has evolved over recent years and is moving into adolescence. The availability of a dedicated VIST dataset for the task has mainstreamed research for visual storytelling and related sub-tasks. This thesis work systematically reports the developments of standard captioning as a parent task with accompanying facets like dense captioning and gradually delves into the domain of visual storytelling. Existing models proposed for VIST are described by examining respective characteristics and scope. All the methods for VIST adapt from the typical encoder-decoder style design, owing to its success in addressing the standard image captioning task. Several subtle differences in the underlying intentions of these methods for approaching the VIST are subsequently summarized. Additionally, alternate perspectives around the existing approaches are explored by re-modeling and modifying their learning mechanisms. Experiments with different objective functions are reported with subjective comparisons and relevant results. Eventually, the sub-field of character relationships within storytelling is studied and a novel idea called character-centric storytelling is proposed to account for prospective characters in the extent of data modalities