J4 ›› 2018, Vol. 35 ›› Issue (1): 13-22.

• 图像与信息处理 • 上一篇    下一篇

基于四通道不可分小波的均值漂移目标跟踪方法

刘斌1,郑凯凯2   

  1. 1. 湖北大学计算机与信息工程学院
    2. 湖北大学
  • 收稿日期:2017-03-22 修回日期:2017-06-07 出版日期:2018-01-28 发布日期:2018-04-04
  • 通讯作者: 郑凯凯

Mean shift object tracking method based on four channel non-Separable wavelets

1,   

  • Received:2017-03-22 Revised:2017-06-07 Published:2018-01-28 Online:2018-04-04

摘要: 针对Mean shift跟踪算法中目标模型更新误差累计导致的后续跟踪误差变大,提出一种基于不可分小波的均值漂移跟踪方法。基于不可分小波对目标区域图像进行分解的结果,利用高频提取准确的目标轮廓区域后,将此区域的高频与低频特征值融合,进行Mean Shift跟踪。在跟踪过程中,使用基于轮廓的尺度更新与模型更新,并使用子特征相关系数对目标特征模型自适应更新。实验结果表明该方法在跟踪场景和目标外观变化时跟踪具有实时性与准确性。与未进行图像分割的跟踪方法相比较,具有更好的跟踪精准度。与使用CRF图像分割等方法相比,具有更好的处理速度与精确性。

关键词: 图像分割

Abstract: In order to solve the problem that the cumulative error caused by model updating on mean shift object tracking will become larger in the following tracking, a new object tracking method based on four channel non-separable wavelets is presented. After extracting the accurate edges of the target using the non-separable wavelets decomposition, mixed features of high frequency and low frequency sub-images on the accurate target are used to track object. During tracking, the target model and scale are updated based on the extracting edges information. And the fusion coefficients are updated considering the relativity of the sub-features. The experimental result shows that the proposed method has validity and accuracy when the scene and the shape of the target are changed. Compared with tracking methods without image segmentation, the proposed tracking method is more precise. When comparing with the tracking methods using CRF, it handles more quickly and has less error.

Key words: image segmentation

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