Road traffic behavior unmanned aerial vehicle monitoring system and method based on deep learning

Abstract

The invention discloses a road traffic behavior unmanned aerial vehicle monitoring system and method based on deep learning. The system comprises an acquisition module, a single-camera processing module, a cross-camera matching module and a traffic parameter extraction module; and the single-camera processing module creates single-camera processing sub-modules for each path of video output by theacquisition module so as to perform data processing on the video, wherein each single-camera processing sub-module comprises an image preprocessing module, a vehicle detection module and a vehicle tracking module. According to the method, an unmanned aerial vehicle is used for shooting road traffic conditions; and the traffic states of vehicles in a monitoring area are analyzed; and the analysis involves the calibration of monitoring pictures, a video-based vehicle detection and tracking technology, a geographic position-based cross-camera multi-target trajectory matching method, an algorithmfor analyzing traffic states according to vehicle motion trajectories and the like.

Description

technical field [0001] The invention belongs to the field of intelligent transportation, and in particular relates to a system and method for monitoring road traffic behavior by an unmanned aerial vehicle based on deep learning. Background technique [0002] Intelligent Transportation System (Intelligent Transportation System, referred to as ITS) is the development direction of the future transportation system, and its purpose is to improve the safety and efficiency of transportation. The system uses various related advanced science and technology to integrate the people, vehicles, roads and environment involved in the traffic system to make it play an intelligent role, so that the traffic system can achieve safety, smoothness, low pollution and low energy consumption. Target. The real-time detection and tracking of moving vehicles is one of the core parts of intelligent transportation system. In recent years, with the rapid development of computer hardware technology and ...

AI Technical Summary

Problems solved by technology

[0004] First, the detection of moving vehicles in dynamic scenes is more difficult due to the existence of two independent movements of the vehicle and the background, and the accuracy of the current mainstream target detection technology needs to be improved in this task;

[0005] Second, in the actual traffic environment, the staggering and occlusion between vehicles are too frequent, and the deformation of vehicles under different viewing angles and changes in camera viewing angles bring huge challenges to the vehicle retrieval and tracking technology under multi-camera;

[0006] Third, for the current common roadside image collection, the effective distance of a single camera is limited and cannot cover the entire road section, and the information collected by multiple cameras is difficult to correlate with each other, and it is very difficult to obtain long-distance and accurate vehicle trajectory information

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