Point cloud target detection and tracking algorithm based on K-means and Kalman

Abstract

This paper is mainly about point cloud image and data processing, virtualization and visualization. An improved K-means iterative clustering algorithm is proposed. In addition, cloth simulation filtering algorithm is used to extract road surface information. On this basis, bilateral filtering and statistic filtering are used to smooth image denoising. Kalman filter is used to track and predict point cloud targets. Using the variance of innovation sequence, the noise variance and observation noise variance can be estimated and corrected gradually in the process of system calculation. The error is relatively small, and the accuracy and reliability are improved. For disordered, occluded, sparsity and noisy, complex terrain image, how to improve the denoising effect, the accuracy and effectiveness of target extraction and tracking is the focus of this paper.