Radar Based Pedestrian Classification using Deep Learning Approach
Abstract
Developments in sensor fusion systems led to extensive research in autonomous vehicles, and object detection is a crucial aspect of vehicle operation. Detecting obstacles can be difficult due to the wide range of potential obstructions, the characteristics of each sensor, and the influence of the surrounding environment. In this paper, the authors use automotive radar data and various neural networks to classify various objects (vehicles, single and multiple people, and bicycles). Combined with the vehicle radar, this research proposed a rapid radar algorithmic implementation for locating, monitoring and extracting micro-Doppler. The authors evaluate three distinct neural network architectures for the five recorded classes of targets: the basic CNN, the residual network, and the combined neural architecture of convolution and recurrent layers. Considerable accuracy is 95.6% immediately before identification of the radar spectrogram from (~0.55 s to produce 0.5 s long spectrogram).
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