Temporal consistent real-time stereo for intelligent vehicles

International audienceThis paper presents a real-time stereo image sequences matching approach dedicated to intelligent vehicles applications. The main idea of the paper consists in integrating temporal information into the matching scheme. The estimation of the disparity map of an actual frame exploits the disparity map estimated for its preceding frame. An association between the two frames is searched, i.e. temporal integration. The disparity range is inferred for the actual frame based on both the association and the disparity map of the preceding frame. Dynamic programming technique is considered for matching the image features. As a similarity measure, a variance-based cost function is used. The proposed approach is tested on virtual and real stereo image sequences and the results are satisfactory. The method is fast and able to provide about 20 millions disparity maps per second on a HP Pavilion dv6700 2.1 GHZ

Análisis de entornos urbanos de tráfico y estimación del movimiento del vehículo para el desarrollo de sistemas avanzados de ayuda a la conducción

Los entornos urbanos de tráfico representan, por su alta complejidad, un desafío para los sistemas inteligentes de transporte, debido a la gran variedad de situaciones y elementos diferentes que pueden acontecer en estos entornos y que deben ser manejadas por estos sistemas. A este respecto, las soluciones presentadas hasta el momento son variadas en lo concerniente a sensores y métodos, obteniendo estos trabajos resultados muy dispares de precisión, complejidad, coste o carga computacional. El trabajo presentado en esta disertación, desarrolla un conjunto de algoritmos y métodos para dar soporte a la implementación de una gran variedad de sistemas avanzados de ayuda a la conducción o navegación autónoma en estos entornos. El sistema descrito se basa en el análisis del entorno del vehículo y la estimación del movimiento del mismo mediante el empleo de un sistema de visión estereoscópica, donde se ha prestado una especial atención, a la hora de definir las características del desarrollo, a posibilitar su implementación en tiempo real. Se ha hecho hincapié tanto en la justificación matemática de los algoritmos presentados, como en la evaluación del efecto de la variación de los valores de configuración de funcionamiento del sistema, evaluándose a su vez los resultados del mismo mediante el empleo de bases de datos de acceso público, analizándose cerca de 11.000 imágenes a lo largo de 9 km de recorrido en entornos urbanos.Traffic urban environments represent, because of its complexity, a challenge for Intelligent Transport Systems due to the great variety of situations and different elements that can happen in these environments and that must be faced by these systems. In connection with this, so far there are a variety of solutions as regards sensors and methods, so the results of precision, complexity, cost or computational load obtained by these works are different. The work presented in this dissertation develops a set of algorithms and methods in order to give support to the implementation of a great variety of advanced driver assistance systems or autonomous navigation in these environments. The system described is based on the analysis of the vehicle environment and its motion estimation by using a stereoscopic vision system which focuses, when defining the characteristics of the development, on enable its implementation in real time. It has been emphasized both the mathematical justification of the algorithms presented and the evaluation of the effect of varying the settings of the system, evaluating its results by using database access public, analyzing around 11,000 images along 9 km in urban environments.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Matilde Santos Peñas.- Secretario: María Araceli Sanchis de Miguel.- Vocal: Felipe Jiménez Alons

Moving object detection for automobiles by the shared use of H.264/AVC motion vectors : innovation report.

Cost is one of the problems for wider adoption of Advanced Driver Assistance Systems (ADAS) in China. The objective of this research project is to develop a low-cost ADAS by the shared use of motion vectors (MVs) from a H.264/AVC video encoder that was originally designed for video recording only. There were few studies on the use of MVs from video encoders on a moving platform for moving object detection. The main contribution of this research is the novel algorithm proposed to address the problems of moving object detection when MVs from a H.264/AVC encoder are used. It is suitable for mass-produced in-vehicle devices as it combines with MV based moving object detection in order to reduce the cost and complexity of the system, and provides the recording function by default without extra cost. The estimated cost of the proposed system is 50% lower than that making use of the optical flow approach. To reduce the area of region of interest and to account for the real-time computation requirement, a new block based region growth algorithm is used for the road region detection. To account for the small amplitude and limited precision of H.264/AVC MVs on relatively slow moving objects, the detection task separates the region of interest into relatively fast and relatively slow speed regions by examining the amplitude of MVs, the position of focus of expansion and the result of road region detection. Relatively slow moving objects are detected and tracked by the use of generic horizontal and vertical contours of rear-view vehicles. This method has addressed the problem of H.264/AVC encoders that possess limited precision and erroneous motion vectors for relatively slow moving objects and regions near the focus of expansion. Relatively fast moving objects are detected by a two-stage approach. It includes a Hypothesis Generation (HG) and a Hypothesis Verification (HV) stage. This approach addresses the problem that the H.264/AVC MVs are generated for coding efficiency rather than for minimising motion error of objects. The HG stage will report a potential moving object based on clustering the planar parallax residuals satisfying the constraints set out in the algorithm. The HV will verify the existence of the moving object based on the temporal consistency of its displacement in successive frames. The test results show that the vehicle detection rate higher than 90% which is on a par to methods proposed by other authors, and the computation cost is low enough to achieve the real-time performance requirement. An invention patent, one international journal paper and two international conference papers have been either published or accepted, showing the originality of the work in this project. One international journal paper is also under preparation