Point Cloud Features-Based Kernel SVM for Human-Vehicle Classification in Millimeter Wave Radar

Abstract

Human-vehicle classification plays an important role in advanced driver assistance systems (ADAS). The use of millimeter wave (mmWave) radar sensor in human-vehicle classification algorithms is of great significance since the sensor maintains to be robust in severe weather (e.g. fog, snow, etc.). To improve classification accuracy under complex scenes of autonomous driving, a new mmWave radar point cloud classification algorithm is proposed in this paper, which realizes human-vehicle classification employing a newly proposed point cloud feature vector with eleven dimensions and based on kernel support vector machine (SVM) classifier. To verify the validity and robustness of the proposed feature vector, a 77 GHz radar is used to collect two datasets for static and moving objects, respectively, with each dataset taken for pedestrians and vehicles at different distances and angles. Experimental results show that the proposed algorithm achieves higher classification accuracy than a conventional one based on signal features. For the comparison based on the same number of dimensions, the number of dimensions of the proposed feature vector is decreased by removing the features with low significance. Experimental results verify that the proposed algorithm maintains advantage over the conventional one.