计算机视觉在车辆交通领域的应用

Computer Vision in Transportation
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Vehicle Classification
Computer Vision applications for automated vehicle classification have a long history. The technologies for automated vehicle classification for vehicle counting have been evolving over the decades. Deep learning methods make it possible to implement large-scale traffic analysis systems using common, inexpensive security cameras.

With rapidly growing affordable sensors such as closed‐circuit television (CCTV) cameras, light detection and ranging (LiDAR), and even thermal imaging devices, vehicles can be detected, tracked, and categorized in multiple lanes simultaneously. The accuracy of vehicle classification can be improved by combining multiple sensors such as thermal imaging, LiDAR imaging with RGB cameras (common surveillance, IP cameras).

In addition, there are multiple specializations; for example, a deep-learning-based computer vision solution for construction vehicle detection has been employed for purposes such as safety monitoring, productivity assessment, and managerial decision-making.

Traffic Computer Vision Vehicles
Vehicle detection and counting using object detection and classification
Moving Violations Detection
Law enforcement agencies and municipalities are increasing the deployment of camera‐based roadway monitoring systems with the goal of reducing unsafe driving behavior. Probably the most critical application is the detection of stopped vehicles in dangerous areas.

Also, there is increasing use of computer vision techniques in smart cities that involve automating the detection of violations such as speeding, running red lights or stop signs, wrong‐way driving, and making illegal turns.

Traffic Flow Analysis
Traffic flow analysis has been studied extensively for intelligent transportation systems (ITS) using invasive methods (tags, under-pavement coils, etc.) and non-invasive methods such as cameras.

With the rise of computer vision and AI, video analytics can now be applied to the ubiquitous traffic cameras, which can generate a vast impact in ITS and smart city. The traffic flow can be observed using computer vision means and measure some of the variables required by traffic engineers.

Parking Occupancy Detection
Visual parking space monitoring is used with the goal of parking lot occupancy detection. Especially in smart cities, computer vision applications power decentralized and efficient solutions for visual parking lot occupancy detection based on a deep Convolutional Neural Network (CNN).

There exist multiple datasets for parking lot detection, such as PKLot and CNRPark-EXT. Furthermore, video-based parking management systems have been implemented using stereoscopic imaging (3D) or thermal cameras. The advantage of camera-based parking lot detection is the scalability for large-scale use, inexpensive maintenance, and installation, especially since it is possible to re-use security cameras.

Vision-based parking lot occupany detection
Vision-based parking lot occupancy detection
Automated License Plate Recognition (ALPR)
Many modern transportations and public safety systems rely on recognizing and extracting license plate information from still images or videos. Automated license plate recognition (ALPR) has in many ways transformed the public safety and transportation industries.

Such number plate recognition systems enable modern tolled roadway solutions, providing tremendous operational cost savings via automation and even enabling completely new capabilities in the marketplace (such as police cruiser‐mounted license plate reading units).

OpenALPR is a popular automatic number-plate recognition library based on optical character recognition (OCR) on images or video feeds of vehicle registration plates.

Vehicle re-identification
With improvements in person re-identification, smart transportation and surveillance systems aim to replicate this approach for vehicles using vision-based vehicle re-identification. Conventional methods to provide a unique vehicle ID are usually intrusive (in-vehicle tag, cellular phone, or GPS).

For controlled settings such as at a toll booth, automatic number-plate recognition (ANPR) is probably the best suitable technology for accurate identification of individual vehicles. However, license plates are subject to change and forgery, and ALPR cannot reflect salient specialties of the vehicles, such as marks or dents.

Non-intrusive methods such as image-based recognition have high potential and demand but are still far from mature for practical usage. Most existing vision-based vehicle re-identification techniques are based on vehicle appearances such as color, texture, and shape.

Today, the recognition of subtle, distinctive features such as vehicle make or year model is still an unresolved challenge.

Pedestrian Detection
The detection of pedestrians is crucial to intelligent transportation systems (ITS). Use cases range from autonomous driving to infrastructure surveillance, traffic management, transit safety and efficiency, and law enforcement.

Pedestrian detection involves many types of sensors, such as traditional CCTV or IP cameras, thermal imaging devices, near‐infrared imaging devices, and onboard RGB cameras. A person detection algorithm, or people detector, can be based on infrared signatures, shape features, gradient features, machine learning, or motion features.

Pedestrian detection relying on deep convolution neural networks (CNN) has made significant progress, even with the detection of heavily occluded pedestrians.

Traffic Sign Detection
Computer Vision applications are used for traffic sign detection and recognition. Vision techniques are applied to segment traffic signs from different traffic scenes (using image segmentation) and employ deep learning algorithms to recognize and classify traffic signs.

Collision Avoidance Systems
Vehicle detection and lane detection form an integral part of most advanced driver assistance systems (ADAS). Deep neural networks have been used recently to investigate deep learning and its use for autonomous collision avoidance systems.

Road Condition Monitoring
Computer vision-based defect detection and condition assessment are developed to monitor concrete and asphalt civil infrastructure. Pavement condition assessment provides information to make more cost-effective and consistent decisions regarding the management of pavement networks.

Generally, pavement distress inspections are performed using sophisticated data collection vehicles and/or foot-on-ground surveys. A Deep Machine Learning Approach to develop an asphalt pavement condition index was developed to provide a human-independent, inexpensive, efficient, and safe way of automated pavement distress detection via Computer Vision.

Another application of computer vision is the visual inspection of roads to detect road potholes and allocate road maintenance with the goal of reducing the number of related vehicle accidents.

Infrastructure Condition Assessment
To ensure civil infrastructure’s safety and serviceability, it is essential to visually inspect and assess its physical and functional condition. Systems for Computer Vision-based civil infrastructure inspection and monitoring automatically convert image and video data into actionable information.

Computer Vision inspection applications are used to identify structural components, characterize local and global visible damage, and detect changes from a reference image. Such monitoring applications include static measurement of strain and displacement and dynamic measurement of displacement for modal analysis.

Driver Attentiveness Detection
Distracted driving detection – such as daydreaming, cell phone usage, and looking at something outside the car – accounts for a large proportion of road traffic fatalities worldwide. Artificial intelligence is used to understand driving behaviors, find solutions to mitigate road traffic incidents.

Road surveillance technologies are used to observe passenger compartment violations, for example, in deep learning based seat belt detection in road surveillance. In‐vehicle driver monitoring technologies focus on visual sensing, analysis, and feedback.

Driver behavior can be inferred both directly from inward driver‐facing cameras and indirectly from outward scene‐facing cameras or sensors. Techniques based on driver-facing video analytics detect the face and eyes with algorithms for gaze direction, head pose estimation, and facial expression monitoring.

Face detection algorithms have been able to detect attentive vs. inattentive faces. Deep Learning algorithms can detect differences between eyes that are focused and unfocused, as well as signs of driving under the influence.

Multiple vision-based applications for real-time distracted driver posture classification with multiple deep learning methods (RNN and CNN) are used in real-time distraction detection.

Computer Vision Application to count vehicles
Computer Vision Application for Vehicle Counting

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