Survey on video anomaly detection in dynamic scenes with moving cameras

The increasing popularity of compact and inexpensive cameras, e.g.~dash cameras, body cameras, and cameras equipped on robots, has sparked a growing interest in detecting anomalies within dynamic scenes recorded by moving cameras. However, existing reviews primarily concentrate on Video Anomaly Detection (VAD) methods assuming static cameras. The VAD literature with moving cameras remains fragmented, lacking comprehensive reviews to date. To address this gap, we endeavor to present the first comprehensive survey on Moving Camera Video Anomaly Detection (MC-VAD). We delve into the research papers related to MC-VAD, critically assessing their limitations and highlighting associated challenges. Our exploration encompasses three application domains: security, urban transportation, and marine environments, which in turn cover six specific tasks. We compile an extensive list of 25 publicly-available datasets spanning four distinct environments: underwater, water surface, ground, and aerial. We summarize the types of anomalies these datasets correspond to or contain, and present five main categories of approaches for detecting such anomalies. Lastly, we identify future research directions and discuss novel contributions that could advance the field of MC-VAD. With this survey, we aim to offer a valuable reference for researchers and practitioners striving to develop and advance state-of-the-art MC-VAD methods.Comment: Under revie

Data Processing for Perception of Autonomous Vehicles in Urban Traffic Environments

The perception system of AVs, consisting of various onboard sensors including cameras and LiDAR scanners, is crucial to perceiving the environment, localizing the vehicle, and recognizing the semantics of traffic scenes. Despite recent advances in computer vision, the perception of AVs has remained challenging, especially in urban environments. One of the reasons is that multiple types of dynamic agents usually coexist in an urban area. The interactions between agents and surroundings are complicated to explicitly model, and the agents' unpredictable behaviors also increase the problem complexity. Moreover, ground truth data with a rich set of labels is not sufficient to cover diverse scenarios, and it is challenging to get data from real scenes. This dissertation presents research contributions to overcome the challenges of AV perception in urban traffic environments, from data collection and labeling to 3D reconstruction and analysis of intrinsic properties. Mainly focusing on unsignalized urban intersections, we discuss (1) how to obtain valuable data from real urban traffic scenes, (2) how to efficiently process the raw data to produce meaningful labels for the dynamic road agents, (3) how to augment the data with semantic labels to the scenes, and (4) what factors make the reconstruction more realistic. We first built a data capture system with a multi-modal sensor suite to simulate actual AV perception. We then introduced a 3D model-fitting algorithm to fit parametrized human mesh models to the pedestrians in a scene. The generated 3D models provide free labels, such as human pose and trajectories, with no cost of manual labeling. We proposed performing the entire scene modeling through densely reconstructing the scene and expanding the scope of automatic labeling to scene elements. These include dynamic vehicles and static components, such as roads, buildings, and traffic signs. To do this, we built a simulator that can generate a rich set of labels using virtual sensors. Finally, we tackled the problem of estimating intrinsic properties and discuss ways to achieve realistic 3D reconstruction. This dissertation understands the AV perception pipeline, explores data preparations at urban traffic scenes, and discusses relevant experiments and applications critical for tackling other problems. We conclude the dissertation with future research directions for further augmenting the data and improving the realism of the reconstructed scene models.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169648/1/wonhui_1.pd

Lidar-based Obstacle Detection and Recognition for Autonomous Agricultural Vehicles

Today, agricultural vehicles are available that can drive autonomously and follow exact route plans more precisely than human operators. Combined with advancements in precision agriculture, autonomous agricultural robots can reduce manual labor, improve workflow, and optimize yield. However, as of today, human operators are still required for monitoring the environment and acting upon potential obstacles in front of the vehicle. To eliminate this need, safety must be ensured by accurate and reliable obstacle detection and avoidance systems.In this thesis, lidar-based obstacle detection and recognition in agricultural environments has been investigated. A rotating multi-beam lidar generating 3D point clouds was used for point-wise classification of agricultural scenes, while multi-modal fusion with cameras and radar was used to increase performance and robustness. Two research perception platforms were presented and used for data acquisition. The proposed methods were all evaluated on recorded datasets that represented a wide range of realistic agricultural environments and included both static and dynamic obstacles.For 3D point cloud classification, two methods were proposed for handling density variations during feature extraction. One method outperformed a frequently used generic 3D feature descriptor, whereas the other method showed promising preliminary results using deep learning on 2D range images. For multi-modal fusion, four methods were proposed for combining lidar with color camera, thermal camera, and radar. Gradual improvements in classification accuracy were seen, as spatial, temporal, and multi-modal relationships were introduced in the models. Finally, occupancy grid mapping was used to fuse and map detections globally, and runtime obstacle detection was applied on mapped detections along the vehicle path, thus simulating an actual traversal.The proposed methods serve as a first step towards full autonomy for agricultural vehicles. The study has thus shown that recent advancements in autonomous driving can be transferred to the agricultural domain, when accurate distinctions are made between obstacles and processable vegetation. Future research in the domain has further been facilitated with the release of the multi-modal obstacle dataset, FieldSAFE

Adv3D: Generating Safety-Critical 3D Objects through Closed-Loop Simulation

Self-driving vehicles (SDVs) must be rigorously tested on a wide range of scenarios to ensure safe deployment. The industry typically relies on closed-loop simulation to evaluate how the SDV interacts on a corpus of synthetic and real scenarios and verify it performs properly. However, they primarily only test the system's motion planning module, and only consider behavior variations. It is key to evaluate the full autonomy system in closed-loop, and to understand how variations in sensor data based on scene appearance, such as the shape of actors, affect system performance. In this paper, we propose a framework, Adv3D, that takes real world scenarios and performs closed-loop sensor simulation to evaluate autonomy performance, and finds vehicle shapes that make the scenario more challenging, resulting in autonomy failures and uncomfortable SDV maneuvers. Unlike prior works that add contrived adversarial shapes to vehicle roof-tops or roadside to harm perception only, we optimize a low-dimensional shape representation to modify the vehicle shape itself in a realistic manner to degrade autonomy performance (e.g., perception, prediction, and motion planning). Moreover, we find that the shape variations found with Adv3D optimized in closed-loop are much more effective than those in open-loop, demonstrating the importance of finding scene appearance variations that affect autonomy in the interactive setting.Comment: CoRL 2023. Project page: https://waabi.ai/adv3d

Learning Birds-Eye View Representations for Autonomous Driving

Over the past few years, progress towards the ambitious goal of widespread fully-autonomous vehicles on our roads has accelerated dramatically. This progress has been spurred largely by the success of highly accurate LiDAR sensors, as well the use of detailed high-resolution maps, which together allow a vehicle to navigate its surroundings effectively. Often, however, one or both of these resources may be unavailable, whether due to cost, sensor failure, or the need to operate in an unmapped environment. The aim of this thesis is therefore to demonstrate that it is possible to build detailed three-dimensional representations of traffic scenes using only 2D monocular camera images as input. Such an approach faces many challenges: most notably that 2D images do not provide explicit 3D structure. We overcome this limitation by applying a combination of deep learning and geometry to transform image-based features into an orthographic birds-eye view representation of the scene, allowing algorithms to reason in a metric, 3D space. This approach is applied to solving two challenging perception tasks central to autonomous driving. The first part of this thesis addresses the problem of monocular 3D object detection, which involves determining the size and location of all objects in the scene. Our solution was based on a novel convolutional network architecture that processed features in both the image and birds-eye view perspective. Results on the KITTI dataset showed that this network outperformed existing works at the time, and although more recent works have improved on these results, we conducted extensive analysis to find that our solution performed well in many difficult edge-case scenarios such as objects close to or distant from the camera. In the second part of the thesis, we consider the related problem of semantic map prediction. This consists of estimating a birds-eye view map of the world visible from a given camera, encoding both static elements of the scene such as pavement and road layout, as well as dynamic objects such as vehicles and pedestrians. This was accomplished using a second network that built on the experience from the previous work and achieved convincing performance on two real-world driving datasets. By formulating the maps as an occupancy grid map (a widely used representation from robotics), we were able to demonstrate how predictions could be accumulated across multiple frames, and that doing so further improved the robustness of maps produced by our system.Toyota Motors Europ

Data-centric Design and Training of Deep Neural Networks with Multiple Data Modalities for Vision-based Perception Systems

224 p.Los avances en visión artificial y aprendizaje automático han revolucionado la capacidad de construir sistemas que procesen e interpreten datos digitales, permitiéndoles imitar la percepción humana y abriendo el camino a un amplio rango de aplicaciones. En los últimos años, ambas disciplinas han logrado avances significativos,impulsadas por los progresos en las técnicas de aprendizaje profundo(deep learning). El aprendizaje profundo es una disciplina que utiliza redes neuronales profundas (DNNs, por sus siglas en inglés) para enseñar a las máquinas a reconocer patrones y hacer predicciones basadas en datos. Los sistemas de percepción basados en el aprendizaje profundo son cada vez más frecuentes en diversos campos, donde humanos y máquinas colaboran para combinar sus fortalezas.Estos campos incluyen la automoción, la industria o la medicina, donde mejorar la seguridad, apoyar el diagnóstico y automatizar tareas repetitivas son algunos de los objetivos perseguidos.Sin embargo, los datos son uno de los factores clave detrás del éxito de los algoritmos de aprendizaje profundo. La dependencia de datos limita fuertemente la creación y el éxito de nuevas DNN. La disponibilidad de datos de calidad para resolver un problema específico es esencial pero difícil de obtener, incluso impracticable,en la mayoría de los desarrollos. La inteligencia artificial centrada en datos enfatiza la importancia de usar datos de alta calidad que transmitan de manera efectiva lo que un modelo debe aprender. Motivada por los desafíos y la necesidad de los datos, esta tesis formula y valida cinco hipótesis sobre la adquisición y el impacto de los datos en el diseño y entrenamiento de las DNNs.Específicamente, investigamos y proponemos diferentes metodologías para obtener datos adecuados para entrenar DNNs en problemas con acceso limitado a fuentes de datos de gran escala. Exploramos dos posibles soluciones para la obtención de datos de entrenamiento, basadas en la generación de datos sintéticos. En primer lugar, investigamos la generación de datos sintéticos utilizando gráficos 3D y el impacto de diferentes opciones de diseño en la precisión de los DNN obtenidos. Además, proponemos una metodología para automatizar el proceso de generación de datos y producir datos anotados variados, mediante la replicación de un entorno 3D personalizado a partir de un archivo de configuración de entrada. En segundo lugar, proponemos una red neuronal generativa(GAN) que genera imágenes anotadas utilizando conjuntos de datos anotados limitados y datos sin anotaciones capturados en entornos no controlados