Time-space image-based vehicle counting method applied to urban traffic scene

Abstract

The invention discloses a time-space image-based vehicle counting method applied to an urban traffic scene. Firstly, an initialization method of a background model is provided; the traffic states of the pixel points of a current scene are judged; the confidence of pixel points in the background model is calculated, and whether the background model is updated is judged; the background model is updated according to a threshold self-adaption updating method used for a current traffic state, and a pixel-based self-adaption segmentation method is utilized to detect foreground; the results of foreground and background detection on a virtual detection straight line are maintained accumulatively so as to form a time-space image; and morphological filtering processing is performed on the time-space image, so that connected regions can be obtained, and the number of the connected regions is calculated, so that the number of vehicles can be obtained. With the method of the invention adopted, the problem of vehicle counting in a complex scene can be solved, and counting can be more accurate. The method is simple and is high in detection speed.

Description

technical field [0001] The patent of the present invention relates to the field of intelligent transportation research, especially the research on vehicle counting methods in complex urban traffic scenes. Background technique [0002] In recent years, as an important part of intelligent transportation systems and smart cities, the intelligence of urban traffic has received more attention. At present, video sensors are installed at many traffic checkpoints in the city, and thousands of videos are generated every day. However, in urban traffic, the traffic density is high, the traffic congestion is serious, and the road users are diverse. The number of vehicles in the foreground of the movement obtained from the complex background of urban traffic is of great significance to the alleviation of urban traffic congestion. However, finding statistics The approach to the number of vehicles remains a challenge. [0003] At present, the method of counting vehicles is mainly the sens...

AI Technical Summary

Problems solved by technology

[0002] In recent years, as an important part of intelligent transportation systems and smart cities, the intelligence of urban traffic has received more attention. At present, video sensors are installed at many traffic checkpoints in the city, and thousands of videos are generated every day. However, in urban traffic, the traffic density is high, the traffic congestion is serious, and the road users are diverse. The number of vehicles in the foreground of the movement obtained from the complex background of urban traffic is of great significance to the alleviation of urban traffic congestion. However, finding statistics The methodology for the number of vehicles remains a challenge

[0003] At present, the method of counting vehicles is mainly the sensing coil. However, the installation of the sensing coil is complicated and the maintenance is difficult. Recently, a video-based detection method has been proposed. The key to the video-based method is the detection of vehicle targets. Algorithms include frame difference method, background difference method, optical flow method, etc.

The frame difference method mainly compares the difference between consecutive frames in the video sequence. The method is simple and the detection speed is fast, but the detection effect is poor when the light changes or the vehicle stops. The optical flow method is based on the motion projected onto the image surface , but this method is sensitive to noise and has a large amount of calculation, so it is not suitable for real-time vehicle detection. The background difference method is very effective for the video target detection of fixed-installed cameras. This method builds a background model and inputs the video The frame is compared with the current background model, and the area with a large difference is marked as the foreground

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