In general, in order to ensure the reasonable and legal use of HOV lanes, save labor and improve efficiency, the mature HOV system will adopt the technology to monitor the HOV lanes and automatically identify the number of vehicle passengers on the HOV lane. That is, the HOV system photographs the front of the vehicle through an electronic policeman and performs algorithm processing. First, the vehicle is positioned, the window is positioned, the passenger's face is monitored in the window area to achieve passenger counting, and the passenger counting and the license plate recognition algorithm are combined. Realize automatic monitoring of HOV lanes. Among the technologies involved are vehicle detection, window detection and vehicle license plate recognition.
Vehicle detection technology is the key to traffic monitoring system and can be realized by frame difference method, background difference method, road marking method and edge detection method.
The frame difference method subtracts two adjacent frames to filter out still scenes in the image, leaving only moving objects. The advantage of this method is that it is not sensitive to new changes in the environment. The disadvantage is that the stationary vehicle cannot be detected, and because the image sampling frequency of the system is fixed, the detection effect is affected by the vehicle speed. Vehicles that are too slow or too fast may cause detection. error.
The background difference method calculates that the current input frame and the background image are only poor to detect foreground objects. The background difference method can detect stationary vehicles, but the disadvantages are accumulation of errors in background updates and changes and shadows to ambient light.
The pavement marking method is based on whether the pavement marking is covered to detect the presence or absence of the vehicle, and the influence of light and shadow can be avoided, but the disadvantage is that the marking on the road surface is required, and the marking on the road is not allowed to be added arbitrarily, so in the system There are great limitations in the installation.
The edge detection method has a slightly higher robustness to ambient light changes than the background difference method. Different parts of the car body, color and the like provide more edge information. Even vehicles with similar color to the road surface have more reflection than the ground. The light can be detected by edge detection.
Window detection method
The window positioning is to separate the window from the image of the car as a target that is independent of the rest of the body. At present, the positioning of the window is mainly applied to the real-time vehicle identification system based on video image. The result of the window positioning is to determine the size of the window and the relative position in the whole vehicle, and transmit it as a characteristic parameter to the subsequent vehicle identification system. And face detection system.
Vehicle license plate recognition technology
At present, there are two main methods for realizing automatic vehicle identification system: one is to use radio technology, and the electromagnetic wave transmitting device with a certain frequency is installed on the vehicle, and the detecting device is installed at the required testing location to realize automatic identification of the vehicle. Another method is to capture images through a camera and apply image processing, image analysis and pattern recognition techniques to automatically identify the vehicle license plate number.
Automatic license plate recognition system based on radio technology
Among such automatic license plate recognition systems, the most widely used is radio frequency technology. Radio frequency identification technology automatically identifies target objects through radio frequency signals and acquires relevant data. The RFID system usually consists of a radio frequency identification tag, an RF reader, and a computer. The RFID tag stores information about the vehicle and is usually placed on the vehicle that needs to be identified. The information it stores is usually contactlessly read and written by the RF reader. When the vehicle equipped with the RFID tag enters the working range of the RF reader, the RFID tag is activated, and its own information is transmitted through the antenna, received by the RFID reader of the identification system, and sent to the computer for processing. .
License plate automatic system based on digital image processing and pattern recognition
The vehicle image is collected by the video capture system, and the license plate number in the image is read out through digital image processing and character recognition. This method does not require the installation of additional identification marks or equipment on the vehicle, but directly utilizes the existing photographing resources to collect non-contact information of the license plate and intelligently identify it in real time, thus having no influence on the vehicle. Secondly, it can also make full use of the existing video equipment on the road, saving equipment and installation, and easy maintenance.
With the development of video image processing technology, the automatic recognition technology of license plate based on digital image processing shows greater advantages and plays an increasingly important role in the field of intelligent transportation.
Although the development of the domestic HOV lane system is relatively slow, but it is becoming more technical, it will be an inevitable trend that more and more technologies will be used to save manpower. We cannot ignore the technologies that can be applied because of the speed of development, nor can we limit the development of HOV lanes in these countries. We should continue to innovate and break through and become an important branch of intelligent transportation.
In recent years, with the rise of deep learning technology, the face recognition application and vehicle feature recognition application are slowly integrated into the intelligent traffic, we can fully expect the integration of deep learning and HOV lane system, making intelligent traffic smarter. Let HOV Lane truly optimize road resources.
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