摘要
提出了一种基于字典学习的运动目标检测方法.该方法首先使用多帧平均方法从训练样本中得到初始背景,再通过BP算法建立背景的初始稀疏表示模型;然后利用视频序列中当前时刻的近邻五帧图像,通过K-SVD方法自适应更新背景数据字典中的原子,使背景稀疏表示模型最优逼近近邻帧背景的观测值;最后将当前帧图像与背景模型进行差分,完成前景运动目标的检测.仿真和对比实验结果表明,对图像信号进行稀疏表示可以有效降低数据的冗余度,减小运行时间,同时在字典更新阶段利用近邻帧图像的相关性特性,能获得鲁棒性较好的背景字典,自动排除伪前景的干扰,从而提高视频序列中的运动目标检测的准确率.
A moving object detection algorithm based on the theory of dictionary learning is proposed .Firstly ,the algorithm gets an initial background image from the training samples by multi-frame averaging algorithm ,and then the initial background sparse representation model is built upon it by BP algorithm .Secondly ,combining with the current adjacent five frames ,the dictionary is updated adaptively by K-SVD method in order to make the background model approximate adjacent frames background′s observation values optimally . Finally , the foreground moving object is obtained by subtracting the background model from the current image . Simulation and comparison experimental results demonstrate that the algorithm can not only reduce data redundancy effectively and decrease the running time via sparse representation , but also can obtain a more robust background dictionary and avoid the interference of the pseudo-foreground by making full advantage of correlation of adjacent frames ,and in the end increased the precision rate of moving object detection .
出处
《微电子学与计算机》
CSCD
北大核心
2014年第3期5-8,13,共5页
Microelectronics & Computer
基金
国家自然科学基金项目(61163056)
江西省科技支撑计划项目(20123BBE50093)
江西省教育厅科技项目(GJJ12306)
江西省研究生创新专项基金项目(YC2012-X015)
关键词
目标检测
背景差分
稀疏表示
字典学习
object detection
background subtraction
sparse representation
dictionary learning
