Feature-based approach for stabilizing videos with moving objects

QIU Jia-tao; LI Yu-shan; CHU Xiu-qin; LIU Yang; NI Le-zhen

doi:10.3788/OPE.20122010.2300

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Feature-based approach for stabilizing videos with moving objects

Article | 更新时间：2020-08-12

- Feature-based approach for stabilizing videos with moving objects
- Optics and Precision Engineering Vol. 20, Issue 10, Pages: 2300-2307(2012)
- 作者机构：
  
  西安电子科技大学电路CAD研究所,陕西西安,710071
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20122010.2300
  CLC： TN941.1
- Received：15 May 2012，
  
  Revised：06 July 2012，
  
  Published：10 October 2012
- 稿件说明：
移动端阅览
邱家涛, 李玉山, 初秀琴, 刘洋, 倪乐真. 稳定运动物体视频的特征方法[J]. 光学精密工程, 2012,20(10): 2300-2307

QIU Jia-tao, LI Yu-shan, CHU Xiu-qin, LIU Yang, NI Le-zhen. Feature-based approach for stabilizing videos with moving objects[J]. Editorial Office of Optics and Precision Engineering, 2012,20(10): 2300-2307
邱家涛, 李玉山, 初秀琴, 刘洋, 倪乐真. 稳定运动物体视频的特征方法[J]. 光学精密工程, 2012,20(10): 2300-2307 DOI： 10.3788/OPE.20122010.2300.

QIU Jia-tao, LI Yu-shan, CHU Xiu-qin, LIU Yang, NI Le-zhen. Feature-based approach for stabilizing videos with moving objects[J]. Editorial Office of Optics and Precision Engineering, 2012,20(10): 2300-2307 DOI： 10.3788/OPE.20122010.2300.

摘要

将背景特征块配对和直方图聚类运动矢量滤波相结合

提出了一种稳定运动物体视频的方法。用得到的全局运动参量补偿摄像机的帧间运动

运用帧差法分割出前景块和背景块

把参考帧背景上的特征块与当前帧上的特征块配对估计下一轮的帧间运动。用一块对多块的匹配策略

把参考块和以参考块为中心的搜索窗内的当前帧上的最佳匹配块配对

建立稀疏运动矢量场。然后

运用直方图聚类方法滤除矢量场中未分离出的前景块矢量和误匹配矢量。最后

用多个包含运动物体的实际视频序列对提出方法进行测试

并与其它先进稳像算法和技术仿真比较。结果表明:提出方法的全局平均帧间变换保真度可达31.05 dB

接近或优于上述先进算法和技术。提出的方法对包含运动物体的视频具有较强的鲁棒性

可以去除帧间高频抖动并有效改善视频质量。

Abstract

By combining a background feature block matching and a histogram clustering method

a new approach for stabilizing videos with moving objects was proposed. The estimated global motion parameters were used to compensate the inter-frame camera motion

and a frame differencing method was adopted to segment the foreground and background blocks

then the feature blocks on the background of reference frame were matched with that on the current frame to estimate the global motion in the next run. By using a one-block-to-multiple-block matching strategy

the reference feature block was matched with the feature blocks of the current frame in the search window centered on the reference block

thus a sparse motion vector field was built. Then

the un-removed foreground vectors and erroneous vectors in this vector field were filtered out using a histogram clustering method. The proposed approach has been tested by using many real videos with moving objects and compared with other state-of-the-art video stabilization algorithms and techniques. The results indicate that the proposed approach can achieve an inter-frame transformation fidelity value by 31.05 dB

which is as high as those of state-of-the-art algorithms and techniques. Moreover

it has a higher robustness to moving objects and can remove inter-frame high frequency jitters and improve video quality.

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references

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