Target Detection and Tracking with the Fusion of the 4D Millimeter-Wave Radar and Depth Vision
In order to solve the problem of the low vehicle detection accuracy of the existing fusion scheme of the millimeter-wave radar and traditional machine vision in the complex environment,this article firstly uses the 4D millimeter-wave radar to replace the traditional millimeter-wave radar,and uses the adaptive Kalman filtering algo-rithm to filter out radar clutter and track targets.Then,it uses vehicle datasets to train and improve the depth vision network MobileNetV2+SSDLite to improve the accuracy of the visual recognition of vehicles.Finally,it uses a de-cision fusion scheme to complete the fusion of millimeter-wave radar signals and visual signals.By comparing ex-perimental results in different environments,it is shown that the improved scheme can effectively estimate and track the target vehicle,which has good vehicle recognition effects in different environments and has better reliability and robustness.
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