Visual region understanding: unsupervised extraction and abstraction

The ability to gain a conceptual understanding of the world in uncontrolled environments is the ultimate goal of vision-based computer systems. Technological societies today are heavily reliant on surveillance and security infrastructure, robotics, medical image analysis, visual data categorisation and search, and smart device user interaction, to name a few. Out of all the complex problems tackled by computer vision today in context of these technologies, that which lies closest to the original goals of the field is the subarea of unsupervised scene analysis or scene modelling. However, its common use of low level features does not provide a good balance between generality and discriminative ability, both a result and a symptom of the sensory and semantic gaps existing between low level computer representations and high level human descriptions. In this research we explore a general framework that addresses the fundamental problem of universal unsupervised extraction of semantically meaningful visual regions and their behaviours. For this purpose we address issues related to (i) spatial and spatiotemporal segmentation for region extraction, (ii) region shape modelling, and (iii) the online categorisation of visual object classes and the spatiotemporal analysis of their behaviours. Under this framework we propose (a) a unified region merging method and spatiotemporal region reduction, (b) shape representation by the optimisation and novel simplication of contour-based growing neural gases, and (c) a foundation for the analysis of visual object motion properties using a shape and appearance based nearest-centroid classification algorithm and trajectory plots for the obtained region classes. 1 Specifically, we formulate a region merging spatial segmentation mechanism that combines and adapts features shown previously to be individually useful, namely parallel region growing, the best merge criterion, a time adaptive threshold, and region reduction techniques. For spatiotemporal region refinement we consider both scalar intensity differences and vector optical flow. To model the shapes of the visual regions thus obtained, we adapt the growing neural gas for rapid region contour representation and propose a contour simplication technique. A fast unsupervised nearest-centroid online learning technique next groups observed region instances into classes, for which we are then able to analyse spatial presence and spatiotemporal trajectories. The analysis results show semantic correlations to real world object behaviour. Performance evaluation of all steps across standard metrics and datasets validate their performance

Active Information Acquisition With Mobile Robots

The recent proliferation of sensors and robots has potential to transform fields as diverse as environmental monitoring, security and surveillance, localization and mapping, and structure inspection. One of the great technical challenges in these scenarios is to control the sensors and robots in order to extract accurate information about various physical phenomena autonomously. The goal of this dissertation is to provide a unified approach for active information acquisition with a team of sensing robots. We formulate a decision problem for maximizing relevant information measures, constrained by the motion capabilities and sensing modalities of the robots, and focus on the design of a scalable control strategy for the robot team. The first part of the dissertation studies the active information acquisition problem in the special case of linear Gaussian sensing and mobility models. We show that the classical principle of separation between estimation and control holds in this case. It enables us to reduce the original stochastic optimal control problem to a deterministic version and to provide an optimal centralized solution. Unfortunately, the complexity of obtaining the optimal solution scales exponentially with the length of the planning horizon and the number of robots. We develop approximation algorithms to manage the complexity in both of these factors and provide theoretical performance guarantees. Applications in gas concentration mapping, joint localization and vehicle tracking in sensor networks, and active multi-robot localization and mapping are presented. Coupled with linearization and model predictive control, our algorithms can even generate adaptive control policies for nonlinear sensing and mobility models. Linear Gaussian information seeking, however, cannot be applied directly in the presence of sensing nuisances such as missed detections, false alarms, and ambiguous data association or when some sensor observations are discrete (e.g., object classes, medical alarms) or, even worse, when the sensing and target models are entirely unknown. The second part of the dissertation considers these complications in the context of two applications: active localization from semantic observations (e.g, recognized objects) and radio signal source seeking. The complexity of the target inference problem forces us to resort to greedy planning of the sensor trajectories. Non-greedy closed-loop information acquisition with general discrete models is achieved in the final part of the dissertation via dynamic programming and Monte Carlo tree search algorithms. Applications in active object recognition and pose estimation are presented. The techniques developed in this thesis offer an effective and scalable approach for controlled information acquisition with multiple sensing robots and have broad applications to environmental monitoring, search and rescue, security and surveillance, localization and mapping, precision agriculture, and structure inspection

Downstream Task Self-Supervised Learning for Object Recognition and Tracking

This dissertation addresses three limitations of deep learning methods in image and video understanding-based machine vision applications. Firstly, although deep convolutional neural networks (CNNs) are efficient for image recognition applications such as object detection and segmentation, they perform poorly under perspective distortions. In real-world applications, the camera perspective is a common problem that we can address by annotating large amounts of data, thus limiting the applicability of the deep learning models. Secondly, the typical approach for single-camera tracking problems is to use separate motion and appearance models, which are expensive in terms of computations and training data requirements. Finally, conventional multi-camera video understanding techniques use supervised learning algorithms to determine temporal relationships among objects. In large-scale applications, these methods are also limited by the requirement of extensive manually annotated data and computational resources.To address these limitations, we develop an uncertainty-aware self-supervised learning (SSL) technique that captures a model\u27s instance or semantic segmentation uncertainty from overhead images and guides the model to learn the impact of the new perspective on object appearance. The test-time data augmentation-based pseudo-label refinement technique continuously trains a model until convergence on new perspective images. The proposed method can be applied for both self-supervision and semi-supervision, thus increasing the effectiveness of a deep pre-trained model in new domains. Extensive experiments demonstrate the effectiveness of the SSL technique in both object detection and semantic segmentation problems. In video understanding applications, we introduce simultaneous segmentation and tracking as an unsupervised spatio-temporal latent feature clustering problem. The jointly learned multi-task features leverage the task-dependent uncertainty to generate discriminative features in multi-object videos. Experiments have shown that the proposed tracker outperforms several state-of-the-art supervised methods. Finally, we proposed an unsupervised multi-camera tracklet association (MCTA) algorithm to track multiple objects in real-time. MCTA leverages the self-supervised detector model for single-camera tracking and solves the multi-camera tracking problem using multiple pair-wise camera associations modeled as a connected graph. The graph optimization method generates a global solution for partially or fully overlapping camera networks

Dataset of Panoramic Images for People Tracking in Service Robotics

We provide a framework for constructing a guided robot for usage in hospitals in this thesis. The omnidirectional camera on the robot allows it to recognize and track the person who is following it. Furthermore, when directing the individual to their preferred position in the hospital, the robot must be aware of its surroundings and avoid accidents with other people or items. To train and evaluate our robot's performance, we developed an auto-labeling framework for creating a dataset of panoramic videos captured by the robot's omnidirectional camera. We labeled each person in the video and their real position in the robot's frame, enabling us to evaluate the accuracy of our tracking system and guide the development of the robot's navigation algorithms. Our research expands on earlier work that has established a framework for tracking individuals using omnidirectional cameras. We want to contribute to the continuing work to enhance the precision and dependability of these tracking systems, which is essential for the creation of efficient guiding robots in healthcare facilities, by developing a benchmark dataset. Our research has the potential to improve the patient experience and increase the efficiency of healthcare institutions by reducing staff time spent guiding patients through the facility.We provide a framework for constructing a guided robot for usage in hospitals in this thesis. The omnidirectional camera on the robot allows it to recognize and track the person who is following it. Furthermore, when directing the individual to their preferred position in the hospital, the robot must be aware of its surroundings and avoid accidents with other people or items. To train and evaluate our robot's performance, we developed an auto-labeling framework for creating a dataset of panoramic videos captured by the robot's omnidirectional camera. We labeled each person in the video and their real position in the robot's frame, enabling us to evaluate the accuracy of our tracking system and guide the development of the robot's navigation algorithms. Our research expands on earlier work that has established a framework for tracking individuals using omnidirectional cameras. We want to contribute to the continuing work to enhance the precision and dependability of these tracking systems, which is essential for the creation of efficient guiding robots in healthcare facilities, by developing a benchmark dataset. Our research has the potential to improve the patient experience and increase the efficiency of healthcare institutions by reducing staff time spent guiding patients through the facility

Understanding Video Transformers for Segmentation: A Survey of Application and Interpretability

Video segmentation encompasses a wide range of categories of problem formulation, e.g., object, scene, actor-action and multimodal video segmentation, for delineating task-specific scene components with pixel-level masks. Recently, approaches in this research area shifted from concentrating on ConvNet-based to transformer-based models. In addition, various interpretability approaches have appeared for transformer models and video temporal dynamics, motivated by the growing interest in basic scientific understanding, model diagnostics and societal implications of real-world deployment. Previous surveys mainly focused on ConvNet models on a subset of video segmentation tasks or transformers for classification tasks. Moreover, component-wise discussion of transformer-based video segmentation models has not yet received due focus. In addition, previous reviews of interpretability methods focused on transformers for classification, while analysis of video temporal dynamics modelling capabilities of video models received less attention. In this survey, we address the above with a thorough discussion of various categories of video segmentation, a component-wise discussion of the state-of-the-art transformer-based models, and a review of related interpretability methods. We first present an introduction to the different video segmentation task categories, their objectives, specific challenges and benchmark datasets. Next, we provide a component-wise review of recent transformer-based models and document the state of the art on different video segmentation tasks. Subsequently, we discuss post-hoc and ante-hoc interpretability methods for transformer models and interpretability methods for understanding the role of the temporal dimension in video models. Finally, we conclude our discussion with future research directions

Predictive Techniques for Scene Understanding by using Deep Learning in Autonomous Driving

La conducción autónoma es considerada uno de los más grandes retos tecnológicos de la actualidad. Cuando los coches autónomos conquisten nuestras carreteras, los accidentes se reducirán notablemente, hasta casi desaparecer, ya que la tecnología estará testada y no incumplirá las normas de conducción, entre otros beneficios sociales y económicos. Uno de los aspectos más críticos a la hora de desarrollar un vehículo autónomo es percibir y entender la escena que le rodea. Esta tarea debe ser tan precisa y eficiente como sea posible para posteriormente predecir el futuro de esta misma y ayudar a la toma de decisiones. De esta forma, las acciones tomadas por el vehículo garantizarán tanto la seguridad del vehículo en sí mismo y sus ocupantes, como la de los obstáculos circundantes, tales como viandantes, otros vehículos o infraestructura de la carretera. En ese sentido, esta tesis doctoral se centra en el estudio y desarrollo de distintas técnicas predictivas para el entendimiento de la escena en el contexto de la conducción autónoma. Durante la tesis, se observa una incorporación progresiva de técnicas de aprendizaje profundo en los distintos algoritmos propuestos para mejorar el razonamiento sobre qué está ocurriendo en el escenario de tráfico, así como para modelar las complejas interacciones entre la información social (distintos participantes o agentes del escenario, tales como vehículos, ciclistas o peatones) y física (es decir, la información geométrica, semántica y topológica del mapa de alta definición) presente en la escena. La capa de percepción de un vehículo autónomo se divide modularmente en tres etapas: Detección, Seguimiento (Tracking), y Predicción. Para iniciar el estudio de las etapas de seguimiento y predicción, se propone un algoritmo de Multi-Object Tracking basado en técnicas clásicas de estimación de movimiento y asociación validado en el dataset KITTI, el cual obtiene métricas del estado del arte. Por otra parte, se propone el uso de un filtro inteligente basado en información contextual de mapa, cuyo objetivo es monitorizar los agentes más relevantes de la escena en el tiempo, representando estos agentes filtrados la entrada preliminar para realizar predicciones unimodales basadas en un modelo cinemático. Para validar esta propuesta de filtro inteligente se usa CARLA (CAR Learning to Act), uno de los simuladores hiperrealistas para conducción autónoma más prometedores en la actualidad, comprobando cómo al usar información contextual de mapa se puede reducir notablemente el tiempo de inferencia de un algoritmo de tracking y predicción basados en métodos físicos, prestando atención a los agentes realmente relevantes del escenario de tráfico. Tras observar las limitaciones de un modelo de predicción basado en cinemática para la predicción a largo plazo de un agente, los distintos algoritmos de la tesis se centran en el módulo de predicción, usando los datasets Argoverse 1 y Argoverse 2, donde se asume que los agentes proporcionados en cada escenario de tráfico ya están monitorizados durante un cierto número de observaciones. En primer lugar, se introduce un modelo basado en redes neuronales recurrentes (particularmente redes LSTM, Long-Short Term Memory) y mecanismo de atención para codificar las trayectorias pasadas de los agentes, y una representación simplificada del mapa en forma de posiciones finales potenciales en la carretera para calcular las trayectorias futuras unimodales, todo envuelto en un marco GAN (Generative Adversarial Network), obteniendo métricas similares al estado del arte en el caso unimodal. Una vez validado el modelo anterior en Argoverse 1, se proponen distintos modelos base (sólo social, incorporando mapa, y una mejora final basada en Transformer encoder, redes convolucionales 1D y mecanismo de atención cruzada para la fusión de características) precisos y eficientes basados en el modelo de predicción anterior, introduciendo dos nuevos conceptos. Por un lado, el uso de redes neuronales gráficas (particularmente GCN, Graph Convolutional Network) para codificar de una forma potente las interacciones de los agentes. Por otro lado, se propone el preprocesamiento de trayectorias preliminares a partir de un mapa con un método heurístico. Gracias a estas entradas y una arquitectura más potente de codificación, los modelos base serán capaces de predecir distintas trayectorias futuras multimodales, es decir, cubriendo distintos posibles futuros para el agente de interés. Los modelos base propuestos obtienen métricas de regresión del estado del arte tanto en el caso multimodal como unimodal manteniendo un claro compromiso de eficiencia con respecto a otras propuestas. El modelo final de la tesis, inspirado en los modelos anteriores y validado en el más reciente dataset para algoritmos de predicción en conducción autónoma (Argoverse 2), introduce varias mejoras para entender mejor el escenario de tráfico y decodificar la información de una forma precisa y eficiente. Se propone incorporar información topológica y semántica de los carriles futuros preliminares con el método heurístico antes mencionado, codificación de mapa basada en aprendizaje profundo con redes GCN, ciclo de fusión de características físicas y sociales, estimación de posiciones finales en la carretera y agregación de su entorno circundante con aprendizaje profundo y finalmente módulo de refinado para mejorar la calidad de las predicciones multimodales finales de un modo elegante y eficiente. Comparado con el estado del arte, nuestro método logra métricas de predicción a la par con los métodos mejor posicionados en el Leaderboard de Argoverse 2, reduciendo de forma notable el número de parámetros y operaciones de coma flotante por segundo. Por último, el modelo final de la tesis ha sido validado en simulación en distintas aplicaciones de conducción autónoma. En primer lugar, se integra el modelo para proporcionar predicciones a un algoritmo de toma de decisiones basado en aprendizaje por refuerzo en el simulador SMARTS (Scalable Multi-Agent Reinforcement Learning Training School), observando en los estudios como el vehículo es capaz de tomar mejores decisiones si conoce el comportamiento futuro de la escena y no solo el estado actual o pasado de esta misma. En segundo lugar, se ha realizado un estudio de adaptación de dominio exitoso en el simulador hiperrealista CARLA en distintos escenarios desafiantes donde el entendimiento de la escena y predicción del entorno son muy necesarios, como una autopista o rotonda con gran densidad de tráfico o la aparición de un usuario vulnerable de la carretera de forma repentina. En ese sentido, el modelo de predicción ha sido integrado junto con el resto de capas de la arquitectura de navegación autónoma del grupo de investigación donde se desarrolla la tesis como paso previo a su implementación en un vehículo autónomo real