Lineal perspective estimation on monocular images

Depth estimation from monocular images can be retrieved from the perspective distortion. One major e ect of this distortion is that a set of parallel lines in the real world converges into a single point in the image plane. The estimation of the coordinates of the vanishing point can be retrieved directly by di erent ways, like Hough Transform and First derivative approaches. Many of them work on speci c real scene characteristics and often lead to spurious vanishing points. Technology and computational advances suggest that some re nements to these simple techniques or a combination of them could lead to more con dent vanishing point detection than modelling and developing a new complicated ones. In this paper we study the behaviour of two classical approaches, introduce them some improvements and propose a new combinational technique to estimate the location of the vanishing point in an image. The solutions will be described and compared, also through the discussion of the results obtained from their application to real images.Presentado en el VIII Workshop Computación Gráfica, Imágenes y Visualización (WCGIV)Red de Universidades con Carreras en Informática (RedUNCI

Lineal perspective estimation on monocular images

Depth estimation from monocular images can be retrieved from the perspective distortion. One major e ect of this distortion is that a set of parallel lines in the real world converges into a single point in the image plane. The estimation of the coordinates of the vanishing point can be retrieved directly by di erent ways, like Hough Transform and First derivative approaches. Many of them work on speci c real scene characteristics and often lead to spurious vanishing points. Technology and computational advances suggest that some re nements to these simple techniques or a combination of them could lead to more con dent vanishing point detection than modelling and developing a new complicated ones. In this paper we study the behaviour of two classical approaches, introduce them some improvements and propose a new combinational technique to estimate the location of the vanishing point in an image. The solutions will be described and compared, also through the discussion of the results obtained from their application to real images.Presentado en el VIII Workshop Computación Gráfica, Imágenes y Visualización (WCGIV)Red de Universidades con Carreras en Informática (RedUNCI

Three dimensional information estimation and tracking for moving objects detection using two cameras framework

Calibration, matching and tracking are major concerns to obtain 3D information consisting of depth, direction and velocity. In finding depth, camera parameters and matched points are two necessary inputs. Depth, direction and matched points can be achieved accurately if cameras are well calibrated using manual traditional calibration. However, most of the manual traditional calibration methods are inconvenient to use because markers or real size of an object in the real world must be provided or known. Self-calibration can solve the traditional calibration limitation, but not on depth and matched points. Other approaches attempted to match corresponding object using 2D visual information without calibration, but they suffer low matching accuracy under huge perspective distortion. This research focuses on achieving 3D information using self-calibrated tracking system. In this system, matching and tracking are done under self-calibrated condition. There are three contributions introduced in this research to achieve the objectives. Firstly, orientation correction is introduced to obtain better relationship matrices for matching purpose during tracking. Secondly, after having relationship matrices another post-processing method, which is status based matching, is introduced for improving object matching result. This proposed matching algorithm is able to achieve almost 90% of matching rate. Depth is estimated after the status based matching. Thirdly, tracking is done based on x-y coordinates and the estimated depth under self-calibrated condition. Results show that the proposed self-calibrated tracking system successfully differentiates the location of objects even under occlusion in the field of view, and is able to determine the direction and the velocity of multiple moving objects

Lineal perspective estimation on monocular images

Depth estimation from monocular images can be retrieved from the perspective distortion. One major e ect of this distortion is that a set of parallel lines in the real world converges into a single point in the image plane. The estimation of the coordinates of the vanishing point can be retrieved directly by di erent ways, like Hough Transform and First derivative approaches. Many of them work on speci c real scene characteristics and often lead to spurious vanishing points. Technology and computational advances suggest that some re nements to these simple techniques or a combination of them could lead to more con dent vanishing point detection than modelling and developing a new complicated ones. In this paper we study the behaviour of two classical approaches, introduce them some improvements and propose a new combinational technique to estimate the location of the vanishing point in an image. The solutions will be described and compared, also through the discussion of the results obtained from their application to real images.Presentado en el VIII Workshop Computación Gráfica, Imágenes y Visualización (WCGIV)Red de Universidades con Carreras en Informática (RedUNCI

Exploitation d'indices visuels liés au mouvement pour l'interprétation du contenu des séquences vidéos

L'interprétation du contenu des séquences vidéo est un des principaux domaines de recherche en vision artificielle. Dans le but d'enrichir l'information provenant des indices visuels qui sont propres à une seule image, on peut se servir d'indices découlant du mouvement entre les images. Ce mouvement peut être causé par un changement d'orientation ou de position du système d'acquisition, par un déplacement des objets dans la scène, et par bien d'autres facteurs. Je me suis intéressé à deux phénomènes découlant du mouvement dans les séquences vidéo. Premièrement, le mouvement causé par la caméra, et comment il est possible de l'interpréter par une combinaison du mouvement apparent entre les images, et du déplacement de points de fuite dans ces images. Puis, je me suis intéressé à la détection et la classification du phénomène d'occultation, qui est causé par le mouvement dans une scène complexe, grâce à un modèle géométrique dans le volume spatio-temporel. Ces deux travaux sont présentés par le biais de deux articles soumis pour publication dans des revues scientifiques

Vision-based traffic monitoring system with hierarchical camera auto-calibration

Texto en inglés.En las últimas décadas, el tráfico, debido al aumento de su volumen y al consiguiente incremento en la demanda de infraestructuras de transporte, se ha convertido en un gran problema en ciudades de casi todo el mundo. Constituye un fenómeno social, económico y medioambiental en el que se encuentra inmersa toda la sociedad, por lo que resulta importante tomarlo como un aspecto clave a mejorar. En esta línea, y para garantizar una movilidad segura, fluida y sostenible, es importante analizar el comportamiento e interacción de los vehículos y peatones en diferentes escenarios. Hasta el momento, esta tarea se ha llevado a cabo de forma limitada por operarios en los centros de control de tráfico. Sin embargo, el avance de la tecnología, sugiere una evolución en la metodología hacia sistemas automáticos de monitorización y control. Este trabajo se inscribe en el marco de los Sistemas Inteligentes de Transporte (ITS), concretamente en el ámbito de la monitorización para la detección y predicción de incidencias (accidentes, maniobras peligrosas, colapsos, etc.) en zonas críticas de infraestructuras de tráfico, como rotondas o intersecciones. Para ello se propone el enfoque de la visión artificial, con el objetivo de diseñar un sistema sensor compuesto de una cámara, capaz de medir de forma robusta parámetros correspondientes a peatones y vehículos que proporcionen información a un futuro sistema de detección de incidencias, control de tráfico, etc.El problema general de la visión artificial en este tipo de aplicaciones, y que es donde se hace hincapié en la solución propuesta, es la adaptabilidad del algoritmo a cualquier condición externa. De esta forma, cambios en la iluminación o en la meteorología, inestabilidades debido a viento o vibraciones, oclusiones, etc. son compensadas. Además el funcionamiento es independiente de la posición de la cámara, con la posibilidad de utilizar modelos con pan-tilt-zoom variable para aumentar la versatilidad del sistema. Una de las aportaciones de esta tesis es la extracción y uso de puntos de fuga (a partir de elementos estructurados de la escena), para obtener una calibración de la cámara sin conocimiento previo. Esta calibración proporciona un tamaño aproximado de los objetos buscados, mejorando así el rendimiento de las siguientes etapas del algoritmo. Para segmentar la imagen se realiza una extracción de los objetos móviles a partir del modelado del fondo, basándose en mezcla de Gaussianas (GMM) y métodos de detección de sombras. En cuanto al seguimiento de los objetos segmentados, se desecha la idea tradicional de considerarlos un conjunto. Para ello se extraen características cuya evolución es analizada para conseguir finalmente una agrupación óptima que sea capaz de solventar oclusiones. El sistema ha sido probado en condiciones de tráfico real sin ningún conocimiento previo de la escena, con resultados bastante satisfactorios que muestran la viabilidad del método

Automatic 2D-to-3D conversion of single low depth-of-field images

This research presents a novel approach to the automatic rendering of 3D stereoscopic disparity image pairs from single 2D low depth-of-field (LDOF) images. Initially a depth map is produced through the assignment of depth to every delineated object and region in the image. Subsequently the left and right disparity images are produced through depth imagebased rendering (DIBR). The objects and regions in the image are initially assigned to one of six proposed groups or labels. Labelling is performed in two stages. The first involves the delineation of the dominant object-of-interest (OOI). The second involves the global object and region grouping of the non-OOI regions. The matting of the OOI is also performed in two stages. Initially the in focus foreground or region-of-interest (ROI) is separated from the out of focus background. This is achieved through the correlation of edge, gradient and higher-order statistics (HOS) saliencies. Refinement of the ROI is performed using k-means segmentation and CIEDE2000 colour-difference matching. Subsequently the OOI is extracted from within the ROI through analysis of the dominant gradients and edge saliencies together with k-means segmentation. Depth is assigned to each of the six labels by correlating Gestalt-based principles with vanishing point estimation, gradient plane approximation and depth from defocus (DfD). To minimise some of the dis-occlusions that are generated through the 3D warping sub-process within the DIBR process the depth map is pre-smoothed using an asymmetric bilateral filter. Hole-filling of the remaining dis-occlusions is performed through nearest-neighbour horizontal interpolation, which incorporates depth as well as direction of warp. To minimising the effects of the lateral striations, specific directional Gaussian and circular averaging smoothing is applied independently to each view, with additional average filtering applied to the border transitions. Each stage of the proposed model is benchmarked against data from several significant publications. Novel contributions are made in the sub-speciality fields of ROI estimation, OOI matting, LDOF image classification, Gestalt-based region categorisation, vanishing point detection, relative depth assignment and hole-filling or inpainting. An important contribution is made towards the overall knowledge base of automatic 2D-to-3D conversion techniques, through the collation of existing information, expansion of existing methods and development of newer concepts