Determining the relative position of vehicles considering bidirectional traffic scenarios in VANETS

Researchers pertaining to both academia and industry have shown strong interest in developing and improving the existing critical ITS solutions. In some of the existing solutions, specially the ones that aim at providing context aware services, the knowledge of relative positioning of one node by other nodes becomes crucial. In this paper we explore, apart from the conventional use of GPS data, the applicability of image processing to aid in determining the relative positions of nodes in a vehicular network. Experiments conducted show that both the use of location information and image processing works well and can be deployed depending on the requirement of the application. Our experiments show that the results that used location information were affected by GPS errors, while the use of image processing, although producing more accurate results, require significantly more processing power

Detección visual de vehículos automotrices en ambientes reales

Entre los algoritmos de detección de objetos, cascade ha demostrado ser uno de los más robustos y flexibles al ser aplicado sobre un gran número de diferentes tipos de objetos. La detección de rostros fue la primera aplicación, así como para muchos sistemas en producción. De igual forma, uno de los grandes objetivos buscados ha sido el de diseñar un vehículo completamente autónomo y donde la conducción se realice de forma automática sin intervención humana. Es por esto que se ha utilizado la combinación de algoritmos cascade y Adaboost para crear un sistema que sea capaz de detectar vehículos de forma eficiente. Como base para este trabajo, se ha utilizado la implementación de OpenCV, que es un software que se distribuye bajo una licencia open source, la cual ha permitido realizar cambios en la implementación de las características tipo HAAR para agregar una serie de características capaces de aumentar el poder de reconocimiento de vehículos. Estas características, en conjunto con las que originalmente se encuentran implementadas por OpenCV, han permitido mejorar los niveles de detección de vehículos en secuencias de imágenes, además, con los entrenamientos realizados se pudo observar cierta reducción en el número de falsos negativos. De acuerdo con la el esquema de este conjunto de algoritmos, adaboost es el encargado de realizar el entrenamiento; entonces, es durante el entrenamiento que se definen los tipos de características tipo HAAR que se utilizarán tanto en el entrenamiento como durante la etapa de detección. Durante el entrenamiento, dicho conjunto de características sirve únicamente como referencias para generar las ventanas de búsqueda en el proceso de detección.Consejo Nacional de Ciencia y Tecnologí

Video based vehicle detection for advance warning Intelligent Transportation System

Video based vehicle detection and surveillance technologies are an integral part of Intelligent Transportation System (ITS), due to its non-intrusiveness and capability or capturing global and specific vehicle behavior data. The initial goal of this thesis is to develop an efficient advance warning ITS system for detection of congestion at work zones and special events based on video detection. The goals accomplished by this thesis are: (1) successfully developed the advance warning ITS system using off-the-shelf components and, (2) Develop and evaluate an improved vehicle detection and tracking algorithm. The advance warning ITS system developed includes many off-the-shelf equipments like Autoscope (video based vehicle detector), Digital Video Recorders, RF transceivers, high gain Yagi antennas, variable message signs and interface processors. The video based detection system used requires calibration and fine tuning of configuration parameters for accurate results. Therefore, an in-house video based vehicle detection system was developed using the Corner Harris algorithm to eliminate the need of complex calibration and contrasts modifications. The algorithm was implemented using OpenCV library on a Arcom\u27s Olympus Windows XP Embedded development kit running WinXPE operating system. The algorithm performance is for accuracy in vehicle speed and count is evaluated. The performance of the proposed algorithm is equivalent or better to the Autoscope system without any modifications to calibration and lamination adjustments

Motion tracking on embedded systems: vision-based vehicle tracking using image alignment with symmetrical function.

Cheung, Lap Chi.Thesis (M.Phil.)--Chinese University of Hong Kong, 2007.Includes bibliographical references (leaves 91-95).Abstracts in English and Chinese.Chapter 1. --- INTRODUCTION --- p.1Chapter 1.1. --- Background --- p.1Chapter 1.1.1. --- Introduction to Intelligent Vehicle --- p.1Chapter 1.1.2. --- Typical Vehicle Tracking Systems for Rear-end Collision Avoidance --- p.2Chapter 1.1.3. --- Passive VS Active Vehicle Tracking --- p.3Chapter 1.1.4. --- Vision-based Vehicle Tracking Systems --- p.4Chapter 1.1.5. --- Characteristics of Computing Devices on Vehicles --- p.5Chapter 1.2. --- Motivation and Objectives --- p.6Chapter 1.3. --- Major Contributions --- p.7Chapter 1.3.1. --- A 3-phase Vision-based Vehicle Tracking Framework --- p.7Chapter 1.3.2. --- Camera-to-vehicle Distance Measurement by Single Camera --- p.9Chapter 1.3.3. --- Real Time Vehicle Detection --- p.10Chapter 1.3.4. --- Real Time Vehicle Tracking using Simplified Image Alignment --- p.10Chapter 1.4. --- Evaluation Platform --- p.11Chapter 1.5. --- Thesis Organization --- p.11Chapter 2. --- RELATED WORK --- p.13Chapter 2.1. --- Stereo-based Vehicle Tracking --- p.13Chapter 2.2. --- Motion-based Vehicle Tracking --- p.16Chapter 2.3. --- Knowledge-based Vehicle Tracking --- p.18Chapter 2.4. --- Commercial Systems --- p.19Chapter 3. --- 3-PHASE VISION-BASED VEHICLE TRACKING FRAMEWORK --- p.22Chapter 3.1. --- Introduction to the 3-phase Framework --- p.22Chapter 3.2. --- Vehicle Detection --- p.23Chapter 3.2.1. --- Overview of Vehicle Detection --- p.23Chapter 3.2.2. --- Locating the Vehicle Center - Symmetrical Measurement --- p.25Chapter 3.2.3. --- Locating the Vehicle Roof and Bottom --- p.28Chapter 3.2.4. --- Locating the Vehicle Sides - Over-complete Haar Transform --- p.30Chapter 3.3. --- Vehicle Template Tracking Image Alignment --- p.37Chapter 3.3.5. --- Overview of Vehicle Template Tracking --- p.37Chapter 3.3.6. --- Goal of Image Alignment --- p.41Chapter 3.3.7. --- Alternative Image Alignment - Compositional Image Alignment --- p.42Chapter 3.3.8. --- Efficient Image Alignment - Inverse Compositional Algorithm --- p.43Chapter 3.4. --- Vehicle Template Update --- p.46Chapter 3.4.1. --- Situation of Vehicle lost --- p.46Chapter 3.4.2. --- Template Filling by Updating the positions of Vehicle Features --- p.48Chapter 3.5. --- Experiments and Discussions --- p.49Chapter 3.5. 1. --- Experiment Setup --- p.49Chapter 3.5.2. --- Successful Tracking Percentage --- p.50Chapter 3.6. --- Comparing with other tracking methodologies --- p.52Chapter 3.6.1. --- 1-phase Vision-based Vehicle Tracking --- p.52Chapter 3.6.2. --- Image Correlation --- p.54Chapter 3.6.3. --- Continuously Adaptive Mean Shift --- p.58Chapter 4. --- CAMERA TO-VEHICLE DISTANCE MEASUREMENT BY SINGLE CAMERA --- p.61Chapter 4.1 --- The Principle of Law of Perspective --- p.61Chapter 4.2. --- Distance Measurement by Single Camera --- p.62Chapter 5. --- REAL TIME VEHICLE DETECTION --- p.66Chapter 5.1. --- Introduction --- p.66Chapter 5.2. --- Timing Analysis of Vehicle Detection --- p.66Chapter 5.3. --- Symmetrical Measurement Optimization --- p.67Chapter 5.3.1. --- Diminished Gradient Image for Symmetrical Measurement --- p.67Chapter 5.3.2. --- Replacing Division by Multiplication Operations --- p.71Chapter 5.4. --- Over-complete Haar Transform Optimization --- p.73Chapter 5.4.1. --- Characteristics of Over-complete Haar Transform --- p.75Chapter 5.4.2. --- Pre-compntation of Haar block --- p.74Chapter 5.5. --- Summary --- p.77Chapter 6. --- REAL TIME VEHICLE TRACKING USING SIMPLIFIED IMAGE ALIGNMENT --- p.78Chapter 6.1. --- Introduction --- p.78Chapter 6.2. --- Timing Analysis of Original Image Alignment --- p.78Chapter 6.3. --- Simplified Image Alignment --- p.80Chapter 6.3.1. --- Reducing the Number of Parameters in Affine Transformation --- p.80Chapter 6.3.2. --- Size Reduction of Image A ligmnent Matrixes --- p.85Chapter 6.4. --- Experiments and Discussions --- p.85Chapter 6.4.1. --- Successful Tracking Percentage --- p.86Chapter 6.4.2. --- Timing Improvement --- p.87Chapter 7. --- CONCLUSIONS --- p.89Chapter 8. --- BIBLIOGRAPHY --- p.9

Development of image processing and vision systems with industrial applications

Ph.DDOCTOR OF PHILOSOPH

Feature-Based Probabilistic Data Association for Video-Based Multi-Object Tracking

This work proposes a feature-based probabilistic data association and tracking approach (FBPDATA) for multi-object tracking. FBPDATA is based on re-identification and tracking of individual video image points (feature points) and aims at solving the problems of partial, split (fragmented), bloated or missed detections, which are due to sensory or algorithmic restrictions, limited field of view of the sensors, as well as occlusion situations