Research on the Traffic Event Discovery in Video Surveillance

视频监控系统的广泛运用，为人们在交通管理和安全监督提供了很大的便利，然而这种便利需要耗费巨大的人力物力去干预和监督。随着科学技术的发展，视频监控系统智能化成为解决该问题的研究方向，但是目前针对异常事件发现的视频监控系统智能化仍不足以满足人们的需求。本文在实验室前课题组研究智能视频监控技术的基础上，研究道路交通事件检测技术并构建了一个道路交通事件检测系统。本文的主要工作如下： (1)介绍视频处理中比较常用的运动目标检测方法并在不同场景下对检测效果进行比较，采用了效果较好的混合高斯模型。在阴影检测算法中，通过统计阴影区域像素在的变化用高斯分布进行建模，从而根据概率大小完成对阴影像素的判断。在对运...Video surveillance systems that are used widely can provide people with a great convenience in traffic management and safety oversight, however, this convenience takes enormous human and material resources to intervene and supervise. With the development of science and technology, intelligent video surveillance system is a good solution to solve that problem, but the intelligent video surveillance...学位：工学硕士院系专业：信息科学与技术学院_计算机科学与技术学号：2302013115315

Semantic Management of Urban Traffic Congestion

Urban traffic congestion is a problem which affects the world and is related to the massive urbanization and excessive number of cars on our streets. This causes a variety of problems, from economical/financial and health-related, to environmental warnings caused by high CO2 and NO2 emissions. This paper proposes a novel software engineering solution, which generates a software application aimed at individual drivers on urban roads, in order to help and ease overall congestion. The novelty is twofold. We target individual drivers in order to motivate them to re-think the purpose and goals of each journey they take. Consequently, the proposed software application enables reasoning upon various options an individual driver may have and helps in choosing the best possible solution for an individual. Our software application utilizes reasoning with SWRL enabled OWL ontologies, which can be hosted by any software application we run in our cars, ready to assist in driving, and implemented in Android / iOS environments

Traffic flow estimation and vehicle‐type classification using vision‐based spatial–temporal profile analysis

Vision‐based traffic surveillance plays an important role in traffic management. However, outdoor illuminations, the cast shadows and vehicle variations often create problems for video analysis and processing. Thus, the authors propose a real‐time cost‐effective traffic monitoring system that can reliably perform traffic flow estimation and vehicle classification at the same time. First, the foreground is extracted using a pixel‐wise weighting list that models the dynamic background. Shadows are discriminated utilising colour and edge invariants. Second, the foreground on a specified check‐line is then collected over time to form a spatial–temporal profile image. Third, the traffic flow is estimated by counting the number of connected components in the profile image. Finally, the vehicle type is classified according to the size of the foreground mask region. In addition, several traffic measures, including traffic velocity, flow, occupancy and density, are estimated based on the analysis of the segmentation. The availability and reliability of these traffic measures provides critical information for public transportation monitoring and intelligent traffic control. Since the proposed method only process a small area close to the check‐line to collect the spatial–temporal profile for analysis, the complete system is much more efficient than existing visual traffic flow estimation methods

A Review on Vehicle Classification and Potential Use of Smart Vehicle-Assisted Techniques

Vehicle classification (VC) is an underlying approach in an intelligent transportation system and is widely used in various applications like the monitoring of traffic flow, automated parking systems, and security enforcement. The existing VC methods generally have a local nature and can classify the vehicles if the target vehicle passes through fixed sensors, passes through the short-range coverage monitoring area, or a hybrid of these methods. Using global positioning system (GPS) can provide reliable global information regarding kinematic characteristics; however, the methods lack information about the physical parameter of vehicles. Furthermore, in the available studies, smartphone or portable GPS apparatuses are used as the source of the extraction vehicle’s kinematic characteristics, which are not dependable for the tracking and classification of vehicles in real time. To deal with the limitation of the available VC methods, potential global methods to identify physical and kinematic characteristics in real time states are investigated. Vehicular Ad Hoc Networks (VANETs) are networks of intelligent interconnected vehicles that can provide traffic parameters such as type, velocity, direction, and position of each vehicle in a real time manner. In this study, VANETs are introduced for VC and their capabilities, which can be used for the above purpose, are presented from the available literature. To the best of the authors’ knowledge, this is the first study that introduces VANETs for VC purposes. Finally, a comparison is conducted that shows that VANETs outperform the conventional techniques

A Review on Vehicle Classification and Potential Use of Smart Vehicle-Assisted Techniques

Vehicle classification (VC) is an underlying approach in an intelligent transportation system and is widely used in various applications like the monitoring of traffic flow, automated parking systems, and security enforcement. The existing VC methods generally have a local nature and can classify the vehicles if the target vehicle passes through fixed sensors, passes through the short-range coverage monitoring area, or a hybrid of these methods. Using global positioning system (GPS) can provide reliable global information regarding kinematic characteristics; however, the methods lack information about the physical parameter of vehicles. Furthermore, in the available studies, smartphone or portable GPS apparatuses are used as the source of the extraction vehicle’s kinematic characteristics, which are not dependable for the tracking and classification of vehicles in real time. To deal with the limitation of the available VC methods, potential global methods to identify physical and kinematic characteristics in real time states are investigated. Vehicular Ad Hoc Networks (VANETs) are networks of intelligent interconnected vehicles that can provide traffic parameters such as type, velocity, direction, and position of each vehicle in a real time manner. In this study, VANETs are introduced for VC and their capabilities, which can be used for the above purpose, are presented from the available literature. To the best of the authors’ knowledge, this is the first study that introduces VANETs for VC purposes. Finally, a comparison is conducted that shows that VANETs outperform the conventional techniques