摘要
针对固定监控场景提出了一种运动目标检测与跟踪方案。在运动目标检测中,利用像素梯度及色度均值、方差分布建立并实时更新背景模型。在目标跟踪模块,引入卡尔曼滤波器预测目标参数,合并目标碎片,建立帧间目标匹配矩阵完成目标匹配。通过实际图像序列测试,算法能较好地实现运动目标跟踪,获得运动目标的轨迹,具有良好的实时性和适应环境变化的能力。
A method of moving object detection and tracking in stationary scene is presented. The background model based on the mean and variance of gradient and chromaticity is using for detecting objects. In tracking module, the matrix using for recognition is built between two frames. Kalman filter is used for predicting objects＇ parameters, merging objects＇ fragments, and its predicting parameters is provided for matching objects. The method has been tested on image sequence to show the validity of the approach.
出处
《计算机应用研究》
CSCD
北大核心
2007年第1期199-202,共4页
Application Research of Computers
基金
国家自然科学基金资助项目(60375001)
高校博士点基金资助项目(20030532004)
关键词
背景模型
目标跟踪
卡尔曼滤波
匹配矩阵
Background Model
Tracking Objects
Kalman Filter
Matching Matrix
