Under vehicle perception for high level safety measures using a catadioptric camera system

In recent years, under vehicle surveillance and the classification of the vehicles become an indispensable task that must be achieved for security measures in certain areas such as shopping centers, government buildings, army camps etc. The main challenge to achieve this task is to monitor the under frames of the means of transportations. In this paper, we present a novel solution to achieve this aim. Our solution consists of three main parts: monitoring, detection and classification. In the first part we design a new catadioptric camera system in which the perspective camera points downwards to the catadioptric mirror mounted to the body of a mobile robot. Thanks to the catadioptric mirror the scenes against the camera optical axis direction can be viewed. In the second part we use speeded up robust features (SURF) in an object recognition algorithm. Fast appearance based mapping algorithm (FAB-MAP) is exploited for the classification of the means of transportations in the third part. Proposed technique is implemented in a laboratory environment

Omnidirectional Stereo Vision for Autonomous Vehicles

Environment perception with cameras is an important requirement for many applications for autonomous vehicles and robots. This work presents a stereoscopic omnidirectional camera system for autonomous vehicles which resolves the problem of a limited field of view and provides a 360° panoramic view of the environment. We present a new projection model for these cameras and show that the camera setup overcomes major drawbacks of traditional perspective cameras in many applications

Graph-Based Classification of Omnidirectional Images

Omnidirectional cameras are widely used in such areas as robotics and virtual reality as they provide a wide field of view. Their images are often processed with classical methods, which might unfortunately lead to non-optimal solutions as these methods are designed for planar images that have different geometrical properties than omnidirectional ones. In this paper we study image classification task by taking into account the specific geometry of omnidirectional cameras with graph-based representations. In particular, we extend deep learning architectures to data on graphs; we propose a principled way of graph construction such that convolutional filters respond similarly for the same pattern on different positions of the image regardless of lens distortions. Our experiments show that the proposed method outperforms current techniques for the omnidirectional image classification problem

04251 -- Imaging Beyond the Pinhole Camera

From 13.06.04 to 18.06.04, the Dagstuhl Seminar 04251 ``Imaging Beyond the Pin-hole Camera. 12th Seminar on Theoretical Foundations of Computer Vision\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Dynamic Programming and Skyline Extraction in Catadioptric Infrared Images

International audienceUnmanned Aerial Vehicles (UAV) are the subject of an increasing interest in many applications and a key requirement for autonomous navigation is the attitude/position stabilization of the vehicle. Some previous works have suggested using catadioptric vision, instead of traditional perspective cameras, in order to gather much more information from the environment and therefore improve the robustness of the UAV attitude/position estimation. This paper belongs to a series of recent publications of our research group concerning catadioptric vision for UAVs. Currently, we focus on the extraction of skyline in catadioptric images since it provides important information about the attitude/position of the UAV. For example, the DEM-based methods can match the extracted skyline with a Digital Elevation Map (DEM) by process of registration, which permits to estimate the attitude and the position of the camera. Like any standard cameras, catadioptric systems cannot work in low luminosity situations because they are based on visible light. To overcome this important limitation, in this paper, we propose using a catadioptric infrared camera and extending one of our methods of skyline detection towards catadioptric infrared images. The task of extracting the best skyline in images is usually converted in an energy minimization problem that can be solved by dynamic programming. The major contribution of this paper is the extension of dynamic programming for catadioptric images using an adapted neighborhood and an appropriate scanning direction. Finally, we present some experimental results to demonstrate the validity of our approach

Modeling the environment with egocentric vision systems

Cada vez más sistemas autónomos, ya sean robots o sistemas de asistencia, están presentes en nuestro día a día. Este tipo de sistemas interactúan y se relacionan con su entorno y para ello necesitan un modelo de dicho entorno. En función de las tareas que deben realizar, la información o el detalle necesario del modelo varía. Desde detallados modelos 3D para sistemas de navegación autónomos, a modelos semánticos que incluyen información importante para el usuario como el tipo de área o qué objetos están presentes. La creación de estos modelos se realiza a través de las lecturas de los distintos sensores disponibles en el sistema. Actualmente, gracias a su pequeño tamaño, bajo precio y la gran información que son capaces de capturar, las cámaras son sensores incluidos en todos los sistemas autónomos. El objetivo de esta tesis es el desarrollar y estudiar nuevos métodos para la creación de modelos del entorno a distintos niveles semánticos y con distintos niveles de precisión. Dos puntos importantes caracterizan el trabajo desarrollado en esta tesis: - El uso de cámaras con punto de vista egocéntrico o en primera persona ya sea en un robot o en un sistema portado por el usuario (wearable). En este tipo de sistemas, las cámaras son solidarias al sistema móvil sobre el que van montadas. En los últimos años han aparecido muchos sistemas de visión wearables, utilizados para multitud de aplicaciones, desde ocio hasta asistencia de personas. - El uso de sistemas de visión omnidireccional, que se distinguen por su gran campo de visión, incluyendo mucha más información en cada imagen que las cámara convencionales. Sin embargo plantean nuevas dificultades debido a distorsiones y modelos de proyección más complejos. Esta tesis estudia distintos tipos de modelos del entorno: - Modelos métricos: el objetivo de estos modelos es crear representaciones detalladas del entorno en las que localizar con precisión el sistema autónomo. Ésta tesis se centra en la adaptación de estos modelos al uso de visión omnidireccional, lo que permite capturar más información en cada imagen y mejorar los resultados en la localización. - Modelos topológicos: estos modelos estructuran el entorno en nodos conectados por arcos. Esta representación tiene menos precisión que la métrica, sin embargo, presenta un nivel de abstracción mayor y puede modelar el entorno con más riqueza. %, por ejemplo incluyendo el tipo de área de cada nodo, la localización de objetos importantes o el tipo de conexión entre los distintos nodos. Esta tesis se centra en la creación de modelos topológicos con información adicional sobre el tipo de área de cada nodo y conexión (pasillo, habitación, puertas, escaleras...). - Modelos semánticos: este trabajo también contribuye en la creación de nuevos modelos semánticos, más enfocados a la creación de modelos para aplicaciones en las que el sistema interactúa o asiste a una persona. Este tipo de modelos representan el entorno a través de conceptos cercanos a los usados por las personas. En particular, esta tesis desarrolla técnicas para obtener y propagar información semántica del entorno en secuencias de imágen

Vision-based Navigation and Mapping Using Non-central Catadioptric Omnidirectional Camera

Omnidirectional catadioptric cameras find their use in navigation and mapping, owing to their wide field of view. Having a wider field of view, or rather a potential 360 degree field of view, allows the user to see and move more freely in the navigation space. A catadioptric camera system is a low cost system which consists of a mirror and a camera. A calibration method was developed in order to obtain the relative position and orientation between the two components so that they can be considered as one monolithic system. The position of the system was determined, for an environment using the conditions obtained from the reflective properties of the mirror. Object control points were set up and experiments were performed at different sites to test the mathematical models and the achieved location and mapping accuracy of the system. The obtained positions were then used to map the environment

A Factorization Based Self-Calibration for Radially Symmetric Cameras

The paper proposes a novel approach for planar selfcalibration of radially symmetric cameras. We model these camera images using notions of distortion center and concentric distortion circles around it. The rays corresponding to pixels lying on a single distortion circle form a right circular cone. Each of these cones is associated with two unknowns; optical center and focal length (opening angle). In the central case, we consider all distortion circles to have the same optical center, whereas in the non-central case they have different optical centers lying on the same optical axis. Based on this model we provide a factorization based self-calibration algorithm for planar scenes from dense image matches. Our formulation provides a rich set of constraints to validate the correctness of the distortion center. We also propose possible extensions of this algorithm i