Dynamic Image Recognition Of Intelligent Traffic Vehicle Based On DSP

The advanced vehicle control system is an important of intelligent transportation,which is the development direction of the future transportation system.Intelligent control technology based on digital image processing,machine vision and machine learning plays a key role in advanced vehicle control systems.Autonomous driving is the inevitable result of intelligent transportation,and the machine vision perception technology is the"eye " in autopilot,It can realize the safe driving by intelligently recognizing the dynamic image of road vehicles where the unmanned vehicles are located.This paper mainly studies the intelligent identification technology of vehicle dynamic image on the road surface of unmanned vehicles under high-speed structural road surface,including lane detection,vehicle detection and tracking,lane departure detection and vehicle distance safety detection.For multi-lane detection,the road surface is segmented by using the feature that the gray level difference between the road surface and the dividing line is larger.Then,the line equation and the Catmull-Rom Spline interpolation algorithm are used to fit the lane dividing line.For single-lane detection,the single lane is first effectively segmented based on the HSV color space and Sobel edge extraction method,and then the lane separation coordinate points are extracted in the perspective transformation space and the segmentation line is fitted with a quadratic polynomial.Aiming at the vehicle detection,the HOG+Gentle-Adaboost classification algorithm is firstly used to detect the vehicle in front of the unmanned vehicle,and then the shadow of the vehicle for is detected based on the characteristics of the shadow at the bottom to verify the authenticity of the vehicle area detected by the learning algorithm.For vehicle tracking,the dynamic second-order autoregressive model method is used to predict the state of the vehicle.For the inherent particle degradation problem of particle filtering,this paper innovatively introduces the Thompson_Taylor algorithm to improve the defects of particle degradation and low diversity.The vehicle distance detection model can not only detect the distance between the front vehicle and the unmanned vehicle,but also calculate the deflection angle of the front vehicle relative to the optical axis of the camera.Based on the CCP(The Car’s Current Position)deviation detection algorithm,this paper proposes a method for detecting the deviation of the unmanned vehicle.In this method,the safe zone and alarm zone of the unmanned vehicle are set to realize the timely warning of lane departure..All algorithms in the vehicle dynamic image recognition refer to the Opencv vision library for simulation and analysis,and the algorithm was transplanted to the embedded platform DSP-DM3730 for testing.Experiments show that the algorithm can effectively solve the problems of intelligent detection of lanes,intelligent detection and tracking of vehicles,intelligent detection of lane departure of unmanned vehicles,and intelligent detection of forward collision.