东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2011, Vol. 32 ›› Issue (11): 1534-1537.DOI: -

• 论著 • 上一篇    下一篇

基于水平集运动目标检测与跟踪方法

徐杨;吴成东;陈东岳;赵骥;   

  1. 东北大学信息科学与工程学院;辽宁科技大学软件学院;
  • 收稿日期:2013-06-19 修回日期:2013-06-19 发布日期:2013-04-04
  • 通讯作者: -
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(60874103);;

Moving object detection and tracking using level set

Xu, Yang (1); Wu, Cheng-Dong (1); Chen, Dong-Yue (1); Zhao, Ji (2)   

  1. (1) School of Information Science and Engineering, Northeastern University, Shenyang 110819, China; (2) School of Software, University of Science and Technology Liaoning, Anshan 114051, China
  • Received:2013-06-19 Revised:2013-06-19 Published:2013-04-04
  • Contact: Xu, Y.
  • About author:-
  • Supported by:
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摘要： 针对目前常采用的运动分析检测方法存在的缺点,提出了基于水平集理论的测地线活动轮廓模型与背景差分相结合的运动目标检测方法.该方法使水平集函数免重新初始化,大大减少了曲线演化迭代的次数和运行时间,得到准确的运动目标轮廓.通过与粒子滤波和mean shift跟踪方法的比较,最终采用效率最高、最优的Kalman滤波预测物体的运动轨迹.实验结果表明,该方法对刚性和非刚性两类目标都具有较好的检测与跟踪效果.

关键词: 运动检测, 目标跟踪, 水平集, Kalman滤波, 测地线活动轮廓

Abstract: According to the disadvantages of the moving detection methods which were used commouly, the moving object detection method of combination of geodesics active contour model and background subtraction based on the principle of level set proposed. The level set function is used in the improved method without re-initialization, the number of iteration in the curve evolution and the execution time are significantly reduced, the exact moving object contour is obtained. Through comparison with the particle filtering and mean shift tracking methods, finally the highest efficiency and optimal Kalman filtering was used to predict the moving trajectory of objects. Experimental results showed that the method has better detection and tracking effect on the two kinds of rigid and non-rigid objects.

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