Abstract:

Aiming at the problems of low accuracy and poor real?time performance of the existing vehicle detection algorithm fusing millimeter wave radar and traditional machine vision， multi?target detection and tracking are studied in this paper. Firstly， the millimeter?wave radar data is preprocessed by using threshold screening and the data association of adjacent frames， and an adaptive extended Kalman filter algorithm is proposed for millimeter?wave radar data tracking. Then， for enhancing the accuracy and speed of target detection， the convolutional neural network is trained based on the real?vehicle data set collected to complete multi?vehicle detection with deep vision. Finally， a decision?level fusion strategy is adopted to fuse the information of millimeter?wave radar and deep vision， and a framework for the multi?target detection and tracking of front vehicles is designed. Real vehicle tests under different traffic environment are carried out to verify the designed framework. The results show that the method adopted can detect and track the front vehicles in real time with higher reliability and robustness， compared with the vehicle detection method fusing millimeter wave radar and traditional machine vision.

Key words: vehicle detection, target tracking, millimeter?wave radar, depth vision, adaptive Kalman filter, neural network