摘要
提出了一种应用于智能交通监控系统的运动目标识别和跟踪方法.针对帧间差分提取运动目标的缺陷与不足,提出了一种基于冗余小波变换的运动目标识别算法,即直接在冗余小波变换域提取运动区域,从而检测出运动目标.对于检测出来的运动目标,本文对mean-shift算法进行了改进,采用自适应mean-shift算法,对目标进行跟踪.实验结果表明,本文提出的算法可以有效地提取运动目标,即使目标与背景具有较高的相似度,也可以较准确地提取出前景运动信息,效果要好于传统的帧差法;跟踪目标准确度高,不受目标大小变化的影响.本算法具较高的实用价值和应用前景.
This paper presents a new method for moving object identification and tracking in the intelligent traffic monitoring system. For the shortcomings and deficiencies of the frame-subtraction method, we put forward a redundant wavelet transform based on the moving object recognition algorithm, which directly detects moving objects in the redundant wavelet transform domain. Subsequently, we use an improved adaptive mean-shift algorithm to track the object. Experimental results show that the proposed algorithm can effectively extract the moving object, even though the object is similar to the background. The segmentation results are more effective than the traditional frame-subtraction method, and the object tracking is accurate without the impact of ehanges in the size of the object. Therefore, the proposed algorithm has high practical value and prospects.
出处
《交通运输系统工程与信息》
EI
CSCD
2008年第4期83-88,共6页
Journal of Transportation Systems Engineering and Information Technology
基金
天津市公安交通局科研基金(公科[2005]06)
关键词
交通监控
目标识别
目标跟踪
traffic monitoring
object recognition
object tracking
