计算机视觉在车辆交通领域的应用

Computer Vision in Transportation
请查看我们的智能城市行业报告。

Computer Vision in Transportation
请查看我们的智能城市行业报告。

Vehicle Classification
基于计算机视觉的自动车辆分类技术拥有悠久的历史。随着年代的推移，用于车辆计数的自动车辆分类技术不断演变。深度学习方法使得利用公共且经济的安防摄像头构建大规模交通分析系统成为可能。

With the rapid advancement of economical sensors such as CCTV cameras, LiDAR systems, and thermal imaging devices available at an affordable price point, modern vehicles are now capable of being detected and monitored across multiple lanes simultaneously. By integrating technologies like thermal imaging systems with LiDAR equipped RGB cameras (commonly used in surveillance setups or public security cameras), the precision of vehicle classification can be significantly enhanced.

Furthermore, the system encompasses diverse specialized areas; notably, a deep learning-based computer vision technology specifically designed for construction vehicle detection tasks has been successfully implemented to address safety monitoring activities, enhance productivity efforts, and provide strategic decision support measures.

Traffic Computer Vision Vehicles

Additionally, there has been a growing adoption of computer vision techniques in smart cities, which are increasingly being employed to automate the detection of violations such as exceeding speed limits, violations of traffic signals, driving in the wrong direction, and illegal u-turns.

Traffic Flow Analysis Traffic flow analysis has seen extensive studies in intelligent transportation systems (ITS), employing instruments like tags and under pavement coils. Additionally, non-invasive techniques including cameras have been utilized to complement these efforts.

Due to the rapid development of computer vision and artificial intelligence (AI), video analysis has become increasingly feasible for deployment onto commonly deployed surveillance cameras. This technological advancement has a significant influence on intelligent transportation systems (ITS) and smart cities. The assessment of traffic flow dynamics relies on advanced computer vision techniques to enable the measurement of key parameters that are essential for traffic engineers.

Parking Occupancy Detection
The primary objective of visual parking space monitoring is to achieve accurate assessment of parking lot occupancy. Especially in smart cities, computer vision applications drive the development of decentralized and efficient solutions for accurately assessing visual parking lot occupancy through advanced deep Convolutional Neural Networks (CNNs).

多种数据集可供用于停车泊位检测，例如PKLot和CNRPark-EXT等。此外，基于视频的停车管理系统已通过立体成像（3D）或热成像技术得以实现。基于摄像头的停车检测方法的优势在于其适用于大规模部署、维护成本低廉以及安装便捷的特点。尤其是由于可以利用现有的安全摄像头进行重复利用这一点。

Vision-based object detection technology based on visual observation of parking lots
Vision-based object detection technology for evaluating parking lot occupancy rates
A fully automated system for recognizing license plates, abbreviated as ALPR
Modern intelligent transportation systems depend heavily on the recognition and extraction of license plate information from static images or video feeds. A fully automated system for recognizing license plates, abbreviated as ALPR, has revolutionized the fields of public safety and transportation.

This number plate recognition system offers modern tolled roadway solutions, benefiting automation in reducing operational costs and also offering new market capabilities, such as police cruiser‐mounted license plate reading units.

OpenALPR is a widely used automated plate recognition system that relies on OCR technology to analyze still images and video feeds from vehicle registration plates.

Vehicle re-identification technology is a significant advancement in intelligent transportation systems. As advancements in person recognition have evolved, modern smart transportation and surveillance systems are striving to mirror these advancements by employing vision-based vehicle re-identification techniques. Conventional methods for assigning unique vehicle IDs typically involve intrusive measures such as in-vehicle tags, cellular devices, or GPS tracking systems.

In managed environments like tollbooth operations, automatic number plate recognition (ANPR) is widely regarded as the most effective technology for precise vehicle identification. However, license plates are prone to alteration and counterfeiting. Additionally, ALPR fails to account for key features like scratches or dents on vehicles.

In managed environments like tollbooth operations, automatic number plate recognition (ANPR) is widely regarded as the most effective technology for precise vehicle identification. However, license plates are prone to alteration and counterfeiting. Additionally, ALPR fails to account for key features like scratches or dents on vehicles.

Nonintrusive imaging techniques, including image-based recognition methods, offer significant potential and demand but remain substantially behind their practical application. Among current vision-based approaches for vehicle re-identification, the majority rely on observable features including color, texture, and shape.

Currently, the identification of minute and specific attributes, such as vehicle make or year model, remains a long-standing issue.

Pedestrian Detection
The identification of pedestrians plays a vital role in the development of intelligent transportation systems. Its applications span across various fields such as autonomous driving, infrastructure surveillance, traffic management, transit safety and efficiency assurance, as well as law enforcement operations.

Pedestrian detection employs various types of sensors such as traditional CCTV or Infrared cameras thermal imaging devices near-infrared imaging devices and onboard RGB cameras. A pedestrian detection algorithm or people detector can be based on infrared-based characteristics geometric features textural features machine learning or motion-based analysis.

基于深度卷积神经网络（CNN）的方法在行人检测方面已经取得了显著的进步，在面对复杂遮挡情况下的行人检测中同样表现优异。

Traffic Sign Recognition
Computer vision methods are employed for traffic sign detection and identification. Vision techniques involve separating traffic signs from diverse background scenarios (via image segmentation) and implementing advanced deep learning systems to analyze and categorize them.

Collision Avoidance Mechanisms
Vehicle detection and lane detection are essential components of modern Advanced Driver Assistance Systems (ADAS). Recent advancements in deep neural networks have enabled the exploration of deep learning techniques for the development of autonomous collision avoidance systems.

Condition Monitoring of Roads
Visual technology methods for defect identification and condition evaluation have been developed to monitor concrete and asphalt civil infrastructure. Pavement condition evaluation provides data for making more economically efficient and consistent decisions in pavement network management.

Generally, pavement distress inspections have been conducted by advanced data collection systems and/or on-the-ground inspections. A Deep Machine Learning Approach was developed to create an asphalt pavement condition index. This system offers an automated solution for detecting pavement distress that is both efficient and cost-effective without relying on human intervention.

Another notable application of computer vision is used for visually inspecting roads to identify road defects and determine where maintenance is needed, aiming to minimize the number of associated vehicle accidents.

Infrastructure Condition Assessment
为了保障市政基础设施的安全性和适用性,有必要对其物理状态和功能状况进行视觉检查与评估.基于计算机视觉技术的市政基础设施检测与监控系统能够将图像与视频数据转化为具有操作意义的信息.

Computer Vision-based inspection applications are employed to detect structural components, classify local and global visible damage, and compare with a reference image. Such monitoring applications involve static measurements of strain and displacement as well as dynamic measurements for dynamic analysis.

Driver Awareness Detection
Divergent attention-driven car accident prevention measures, including activities like daydreaming, phone use, and diversionary glances away from the vehicle, account for a significant share of global car accident casualties. Advanced AI technology is employed to analyze and interpret driving behavior patterns, identify methods to reduce accident risks.

采用道路监控技术对乘客 compartment 违反规定的行为进行观察，在这一领域中基于深度学习的安全带检测技术是一个典型应用案例。车内驾驶员监控技术关注于视觉感知、数据分析以及反馈机制。

Driver behavior can be derived both directly from inward driver-facing cameras and indirectly from outward scene-facing cameras or sensors. Techniques based on driver-facing video analytics identify the face and eyes using algorithms for gaze direction, head pose estimation, and monitoring of facial expressions.

Face detection algorithms have achieved the ability to distinguish faces with attentive features from those with inattentive features. Deep Learning algorithms are capable of detecting distinctive eye features that indicate whether eyes are focused versus unfocused, as well as signs of driving under the influence.

Various vision-based approaches for real-time classification of divergent driver postures involving multiple deep learning techniques (RNN and CNN) are employed to detect real-time distractions.

A computer vision application designed to analyze and count vehicles. of vehicle counting.