3D Laser Point Cloud Based Vehicle Target Recognition Algorithm

Vehicle target recognition is an important aspect of intelligent transportation, and the accurate recognition of vehicles is crucial for traffic control and safe driving. In this paper, we propose a vehicle target recognition algorithm based on 3D laser point cloud. In order to reduce the computational cost of data processing, the random sampling consistency algorithm (RANSAC) and two-dimensional rasterization algorithm of point cloud are introduced to reduce the original point cloud data. A density-based clustering algorithm is used to cluster the disordered target point clouds into a number of predefined clusters. According to the unique structural features of the vehicle target, the multidimensional composite feature vectors of the target point cloud clusters are manually extracted as input. The KITTI dataset is used to train the multilayer perceptron (MLP) classification model to achieve accurate recognition of vehicle targets. The results of experiments on validation data sets showed that the proposed algorithm achieved an accuracy of 93.2% for vehicle target recognition.