计算机视觉在车辆交通领域的应用

Computer Vision in Transportation
Read our smart city industry report here.

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 extensi