Shadow detection in still road images using chrominance properties of shadows and spectral power distribution of the illumination

A well-known challenge in vision-based driver assistance systems is cast shadows on the road, which makes fundamental tasks such as road and lane detections difficult. In as much as shadow detection relies on shadow features, in this paper, we propose a set of new chrominance properties of shadows based on the skylight and sunlight contributions to the road surface chromaticity. Six constraints on shadow and non-shadowed regions are derived from these properties. The chrominance properties and the associated constraints are used as shadow features in an effective shadow detection method intended to be integrated on an onboard road detection system where the identification of cast shadows on the road is a determinant stage. Onboard systems deal with still outdoor images; thus, the approach focuses on distinguishing shadow boundaries from material changes by considering two illumination sources: sky and sun. A non-shadowed road region is illuminated by both skylight and sunlight, whereas a shadowed one is illuminated by skylight only; thus, their chromaticity varies. The shadow edge detection strategy consists of the identification of image edges separating shadowed and non-shadowed road regions. The classification is achieved by verifying whether the pixel chrominance values of regions on both sides of the image edges satisfy the six constraints. Experiments on real traffc scenes demonstrated the effectiveness of our shadow detection system in detecting shadow edges on the road and material-change edges, outperforming previous shadow detection methods based on physical features, and showing the high potential of the new chrominance properties

Shadow-based vehicle detection in urban traffic

Vehicle detection is a fundamental task in Forward Collision Avoiding Systems (FACS). Generally, vision-based vehicle detection methods consist of two stages: hypotheses generation and hypotheses verification. In this paper, we focus on the former, presenting a feature-based method for on-road vehicle detection in urban traffic. Hypotheses for vehicle candidates are generated according to the shadow under the vehicles by comparing pixel properties across the vertical intensity gradients caused by shadows on the road, and followed by intensity thresholding and morphological discrimination. Unlike methods that identify the shadow under a vehicle as a road region with intensity smaller than a coarse lower bound of the intensity for road, the thresholding strategy we propose determines a coarse upper bound of the intensity for shadow which reduces false positives rates. The experimental results are promising in terms of detection performance and robustness in day time under different weather conditions and cluttered scenarios to enable validation for the first stage of a complete FACS.This work is funded by the Spanish Ministry of Economy and Competitiveness (Project: DPI2012-36959)

Monovision-based vehicle detection, distance and relative speed measurement in urban traffic

This study presents a monovision-based system for on-road vehicle detection and computation of distance and relative speed in urban traffic. Many works have dealt with monovision vehicle detection, but only a few of them provide the distance to the vehicle which is essential for the control of an intelligent transportation system. The system proposed integrates a single camera reducing the monetary cost of stereovision and RADAR-based technologies. The algorithm is divided in three major stages. For vehicle detection, the authors use a combination of two features: the shadow underneath the vehicle and horizontal edges. They propose a new method for shadow thresholding based on the grey-scale histogram assessment of a region of interest on the road. In the second and third stages, the vehicle hypothesis verification and the distance are obtained by means of its number plate whose dimensions and shape are standardised in each country. The analysis of consecutive frames is employed to calculate the relative speed of the vehicle detected. Experimental results showed excellent performance in both vehicle and number plate detections and in the distance measurement, in terms of accuracy and robustness in complex traffic scenarios and under different lighting conditions

Shadow Detection in Still Road Images Using Chrominance Properties of Shadows and Spectral Power Distribution of the Illumination

A well-known challenge in vision-based driver assistance systems is cast shadows on the road, which makes fundamental tasks such as road and lane detections difficult. In as much as shadow detection relies on shadow features, in this paper, we propose a set of new chrominance properties of shadows based on the skylight and sunlight contributions to the road surface chromaticity. Six constraints on shadow and non-shadowed regions are derived from these properties. The chrominance properties and the associated constraints are used as shadow features in an effective shadow detection method intended to be integrated on an onboard road detection system where the identification of cast shadows on the road is a determinant stage. Onboard systems deal with still outdoor images; thus, the approach focuses on distinguishing shadow boundaries from material changes by considering two illumination sources: sky and sun. A non-shadowed road region is illuminated by both skylight and sunlight, whereas a shadowed one is illuminated by skylight only; thus, their chromaticity varies. The shadow edge detection strategy consists of the identification of image edges separating shadowed and non-shadowed road regions. The classification is achieved by verifying whether the pixel chrominance values of regions on both sides of the image edges satisfy the six constraints. Experiments on real traffic scenes demonstrated the effectiveness of our shadow detection system in detecting shadow edges on the road and material-change edges, outperforming previous shadow detection methods based on physical features, and showing the high potential of the new chrominance properties

Vision-based vehicle detection and location using shadows

RESUMEN: La presente tesis aborda el desarrollo de un sistema basado en visión por computador para la detección y localización de vehículos en la trayectoria. Las hipótesis son generadas de acuerdo con la región sombreada de la carretera debajo del vehículo. Se propone una estrategia nueva que supera importantes dificultades como la presencia de sombras laterales. La verificación de hipótesis se basa en la evaluación de características de la apariencia comunes a todas las traseras de los vehículos. También, se presenta un método para la detección de las sombras proyectadas en la carretera basado en propiedades físicas tanto de la iluminación como de la superficie. El método no requiere calibración de la cámara y considera dos fuentes de iluminación con distintas distribuciones espectrales: la luz del sol y la luz del cielo. El método se centra en distinguir entre los bordes de las sombras y los bordes causados por un cambio de material.ABSTRACT: This thesis addresses a vision-based system for on-road vehicle detection in urban traffic. Hypotheses are generated according to the shadowed road region under the vehicles. We propose a new strategy for the detection of the shadow under a vehicle which overcomes significant difficulties such as the presence of lateral shadows and cluttered roads. The hypotheses verification strategy is based on the evaluation of appearance features common to all vehicle rears. It is also presented a physics-based method for shadow edge detection in road scenes. The method does not require camera calibration and considers two illumination sources with different SPDs: skylight and sunlight. The proposed method focuses on distinguishing shadow boundaries from material changes by comparing pixel properties across image edges

Detección de vehículos basada en visión por computador para sistema de ayuda a la conducción en tráfico urbano. Generación de hipótesis

[Resumen] La detección de vehículos es una tarea fundamental en los sistemas anticolisión frontal. Los métodos de detección de vehículos basados en visión por computador se dividen generalmente en dos etapas: generación de hipótesis y verificación de hipótesis. El presente artículo se centra en la primera, presentando un método de generación de hipótesis basado en la sombra que los vehículos originan bajo sí mismos. La detección de la sombra se realiza mediante un nuevo enfoque de umbralización por intensidad. En primer lugar, un umbral es establecido a partir de la región de la carretera frente al ego-vehículo. Posteriormente, un segundo umbral es empleado sobre cada candidato con el fin de identificar la posible sombra lateral adyacente al vehículo. El método supera importantes dificultades como la presencia de sombras laterales, indicaciones pintadas en el asfalto y fondos de imagen saturados de objetos. Ensayos realizados en tráfico urbano muestran un comportamiento eficiente y fiable del método.Este trabajo ha sido realizado bajo el patrocinio del Ministerio de Economía y Competitividad. (Proyecto: DPI2012-36959)https://doi.org/10.17979/spudc.978849749808

Localización del punto de fuga para sistema de detección de líneas de carril

[Resumen] Una tarea fundamental dentro de los sistemas de detección de cambio de carril involuntario es la detección de las marcas viales longitudinales pintadas en el asfalto, ya sean continuas o discontinuas, así como de los límites de la vía. Los métodos de detección de estas características de la carretera basados en visión por computador, se dividen generalmente en dos etapas: extracción de las potenciales marcas viales longitudinales y ajuste de estas a un modelo matemático. El presente artículo se centra en la primera, presentando un método para la detección del punto (vía recta) o puntos (vía curva) de fuga de la imagen. Los bordes de imagen cuya proyección pase por el punto de fuga, pertenecen a potenciales líneas de carril o límites de la carretera, siendo por lo tanto dicho punto una característica importante para la comprensión de la escena y detección del carril. Tras un filtrado basado en la orientación de los bordes de la imagen, los puntos de fuga se obtienen a partir de un proceso iterativo por votación. Ensayos realizados en tráfico urbano real muestran un comportamiento eficiente del método.Ministerio de Economía y Competitividad; DPI2012-3695