{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:46:07Z","timestamp":1776811567028,"version":"3.51.2"},"reference-count":8,"publisher":"European Society of Computational Methods in Sciences and Engineering","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"published-print":{"date-parts":[[2022,12,19]]},"abstract":"<jats:p>For moving object detection and trajectory prediction in video images, it is necessary to perform image processing, feature extraction, and localization of the object. Therefore, this paper designs an optimized Kalman-Elman (KE) algorithm for trajectory prediction. In order to remove the noise points on the measured values in the Kalman filter algorithm and to solve the problem of the random setting of the initial weights and thresholds of the Elman neural network, we encode the above parameters and improve the two algorithms by using Particle Swarm Optimization (PSO). Quantitative values of the object feature extraction are used as input parameters of the Elman neural network. After a large amount of training, we obtain the predicted position of the moving object finally. The experimental results show that the prediction error of this method is significantly smaller when it is compared with previous methods.<\/jats:p>","DOI":"10.3233\/jcm-226342","type":"journal-article","created":{"date-parts":[[2022,8,23]],"date-time":"2022-08-23T11:27:38Z","timestamp":1661254058000},"page":"2149-2159","source":"Crossref","is-referenced-by-count":0,"title":["Moving object detection and trajectory prediction based on image processing"],"prefix":"10.66113","volume":"22","author":[{"given":"Wei","family":"Qi","sequence":"first","affiliation":[{"name":"Jiangsu Union Technical Institute, Xuzhou, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zihan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Sciences and Technology, China University of Mining and Technology, Xuzhou, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiting","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Sciences and Technology, China University of Mining and Technology, Xuzhou, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiawei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Sciences and Technology, China University of Mining and Technology, Xuzhou, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"55691","reference":[{"issue":"12","key":"10.3233\/JCM-226342_ref1","first-page":"6","article-title":"An overview of target tracking technology","volume":"38","author":"Lu","year":"2018","journal-title":"Ship Electronic Engineering"},{"issue":"1","key":"10.3233\/JCM-226342_ref2","first-page":"51","article-title":"Particle filter object tracking algorithm based on sparse representation and nonlinear resampling","volume":"27","author":"Fan","year":"2018","journal-title":"Journal of Beijing Institute of Technology"},{"issue":"3","key":"10.3233\/JCM-226342_ref3","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1049\/iet-cvi.2017.0176","article-title":"Target tracking approach via quantum genetic algorithm","volume":"12","author":"Jin","year":"2018","journal-title":"IET Computer Vision"},{"issue":"1","key":"10.3233\/JCM-226342_ref7","first-page":"1","article-title":"An efficient and flexible approach for multiple vehicle tracking in the aerial video sequence","author":"Zhang","year":"2018","journal-title":"International Journal of Remote Sensing"},{"key":"10.3233\/JCM-226342_ref8","doi-asserted-by":"crossref","unstructured":"Wu M, Ling H, Bi N, et al. Visual tracking with multiview trajectory prediction. IEEE Transactions on Image Processing. 2020; 29: 8355-8367.","DOI":"10.1109\/TIP.2020.3014952"},{"issue":"2","key":"10.3233\/JCM-226342_ref9","doi-asserted-by":"crossref","first-page":"1463","DOI":"10.1109\/LRA.2021.3056339","article-title":"Bitrap: Bi-directional pedestrian trajectory prediction with multi-modal goal estimation","volume":"6","author":"Yao","year":"2021","journal-title":"IEEE Robotics and Automation Letters"},{"issue":"4","key":"10.3233\/JCM-226342_ref12","first-page":"1","article-title":"Face Recognition and Human Tracking Using GMM, HOG and SVM in Surveillance Videos","author":"Dadi","year":"2017","journal-title":"Annals of Data Science"},{"issue":"5","key":"10.3233\/JCM-226342_ref13","first-page":"1","article-title":"Edge detection: A review of dissimilarity evaluations and a proposed normalized measure","volume":"77","author":"Magnier","year":"2017","journal-title":"Multimedia Tools & Applications"}],"container-title":["Journal of Computational Methods in Sciences and Engineering"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JCM-226342","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:06:34Z","timestamp":1776809194000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JCM-226342"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,19]]},"references-count":8,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.3233\/jcm-226342","relation":{},"ISSN":["1472-7978","1875-8983"],"issn-type":[{"value":"1472-7978","type":"print"},{"value":"1875-8983","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,19]]}}}