井冈山大学学报（自然科学版）Issue(5)：60-65,6.DOI:10.3969/j.issn.1674-8085.2015.05.012
基于改进型Camshift和卡尔曼滤波器的车辆跟踪算法
VEHICLE TRACKING SYSTEM BASED ON IMPROVED CAMSHIFT ALGORITHM AND THE KALMAN FILTER
摘要
Abstract
In the process of vehicle tracking, a new tracking algorithm that combines the improved Camshift and the Kalman filter has been proposed when tracking target loss or failure. Firstly, the Kalman filter is used to estimate the target position in order to overcome the cover of target. We use an improved Camshift algorithm based on the distance of search center to weight every pixel with Gauss model core function in color histogram created by H component. We also achieve the optimal search window by self-adaptive calculation and improve traditional Camshift shortcoming which is powerless for directly resistance noise under the same color background. Finally, the simulations and experiments show that the method improves the accuracy and continuity of vehicle tracking.关键词
车辆跟踪/Camshift算法/卡尔曼滤波/加权高斯模型核函数Key words
vehicle tracking/camshift algorithm/kalman filter/weighted Gauss model core function分类
信息技术与安全科学引用本文复制引用
马平华,徐晓光,夏雯娟,陆涛..基于改进型Camshift和卡尔曼滤波器的车辆跟踪算法[J].井冈山大学学报（自然科学版）,2015,(5):60-65,6.基金项目
安徽省高校省级自然科学研究项目 （）
