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

Autor:	Liangyu Fang, Yue Sun, Qian Luo
Rok vydání:	2021
Předmět:	History Computer science Computer Science::Computer Vision and Pattern Recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION k-means clustering Point cloud Kalman filter Tracking (particle physics) Algorithm Computer Science Applications Education
Zdroj:	Journal of Physics: Conference Series. 1952:022024
ISSN:	1742-6596 1742-6588
DOI:	10.1088/1742-6596/1952/2/022024
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::4d7aa8c94c921d6328261cb91caa0b41 https://doi.org/10.1088/1742-6596/1952/2/022024 Zobrazit plný text záznamu