Kalman滤波融合优化Mean Shift的目标跟踪算法  

Target Tracking Algorithm Based on Kalman Filter and Optimization Mean Shift

作　　者：韩涛[1] 吴衡[1] 张虎龙[1] 侯海啸[1] 邹强[1] 张兴国[1] 

机构地区：[1]中国飞行试验研究院,西安710089

出　　处：《光电工程》2014年第6期56-62,共7页Opto-Electronic Engineering

基　　金：航空科技基金(2010ZD30004)

摘　　要：目标跟踪中,目标的背景变化、形状改变、遮挡,往往会导致跟踪失败,而跟踪的实时性和准确性是必须考虑的问题。本文首先对Mean Shift算法进行了介绍,接着对Mean Shift算法进行了优化:修正Mean Shift算法迭代权值,修正后主要信息贡献更加突出,次要信息受到抑制,避免了开方的繁琐运算,降低了运算量。提出了目标模板更新算法,解决了背景变化和目标形状改变时跟踪失败的问题。然后在水平位置和竖直位置建立Kalman滤波器,同时将优化Mean Shift算法与Kalman滤波融合,解决了目标完全遮挡后无法继续跟踪的问题。仿真实验表明,本文提出的目标跟踪算法在目标遮挡,目标形状改变,目标跟踪失败的情况下具有更高的跟踪精度,更高的实时性和鲁棒性。Background change, shape change and target covering will cause target tracking failure. Real-time and accuracy in target tracking must be considered. First, the Mean Shift algorithm is presented, and then the Mean Shift algorithm iterative weight is modified with main information more prominent, secondary information suppressed, avoiding the tedious root, and improving the real-time and effectiveness of target tracking. The target template updating algorithm is presented to solve change of background and target shape change. Then a Kalman filter in the horizontal position and the vertical position is established to solve the problem of target tracking completely covered. Simulation results show that target tracking algorithm on the condition of target template update has higher tracking accuracy, higher real-time property and at the same time is more robust than the traditional Mean Shift tracking algorithm.

关 键 词：KALMAN滤波 Mean SHIFT算法 目标跟踪 模板更新 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]