Video based multi-vehicle traffic information detection method

Abstract

The invention discloses a video based multi-vehicle traffic information detection method. The method includes the steps of acquiring traffic video; setting parameters; initializing a system; detecting a vehicle object; combining an initial color background image with an RGB (red, green, blue) color image in current frame of a video to obtain a self-adapting real-time dynamic color background image; extracting a color difference result image fgi; segmenting a Otsu threshold in a self adaptation manner; removing shadow off a foreground target image; performing morphological operation and filling car mass; counting cars; judging whether the car is detected by at least one virtual detecting coil or not, if yes, determining that the car is detected by the virtual detecting coil, adding 1 to total number of the cars, and executing the step 5; if not, executing the step 406; and acquiring traffic information of the multiple vehicles. By the aid of the video based multi-vehicle traffic information detection method, every passing car is traced, vehicle type and speed of the car are recorded, traffic information of the multiple vehicles such as flow and average speed of different vehicles are obtained, lane crossing vehicles, adhesion and shielding factors of the vehicles are fully considered, anti-interference performance is strong, and detecting accuracy is high.

Description

technical field [0001] The invention belongs to the field of intelligent transportation, is mainly used for collecting traffic information of various vehicle types on highways and urban roads, and in particular relates to a video-based multi-vehicle traffic information detection method. Background technique [0002] In the application of intelligent transportation system, traffic management, traffic simulation and traffic flow theory research, a very important task is the collection of traffic information. At present, the information that most traffic information collection equipment can provide is mainly macroscopic parameters such as the flow rate, speed, and density of a single vehicle type. These traffic information do not distinguish between vehicle types. Due to the larger vehicle structure and lower power performance, large vehicles show the operating characteristics of low speed and excessive consumption of road space and time during operation, which not only reduces...

AI Technical Summary

Problems solved by technology

[0004] At present, the vast majority of traffic information video collection systems only provide macroscopic traffic information that does not consider the classification of vehicle types, often ignoring cross-lane vehicles, resulting in missed inspections, and also counting the same vehicle repeatedly, which is harmful to factors such as vehicle adhesion and occlusion. The error immunity is poor

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