Moving object detection based on variant block difference in complex scenes

ZHU Juan-juan; GUO Bao-long

doi:10.3788/OPE.20111901.0183

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Moving object detection based on variant block difference in complex scenes

Article | 更新时间：2020-08-12

- Moving object detection based on variant block difference in complex scenes
- Optics and Precision Engineering Vol. 19, Issue 1, Pages: 183-191(2011)
- 作者机构：
  
  西安电子科技大学机电工程学院,陕西西安,710071
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20111901.0183
  CLC： TP391.4
- Received：01 April 2010，
  
  Revised：25 June 2010，
  
  Published Online：22 January 2011，
  
  Published：22 January 2011
- 稿件说明：
移动端阅览
朱娟娟, 郭宝龙. 复杂场景中基于变块差分的运动目标检测[J]. 光学精密工程, 2010,19(1): 183-191

ZHU Juan-juan, GUO Bao-long. Moving object detection based on variant block difference in complex scenes[J]. Editorial Office of Optics and Precision Engineering, 2010,19(1): 183-191
朱娟娟, 郭宝龙. 复杂场景中基于变块差分的运动目标检测[J]. 光学精密工程, 2010,19(1): 183-191 DOI： 10.3788/OPE.20111901.0183.

ZHU Juan-juan, GUO Bao-long. Moving object detection based on variant block difference in complex scenes[J]. Editorial Office of Optics and Precision Engineering, 2010,19(1): 183-191 DOI： 10.3788/OPE.20111901.0183.

摘要

针对复杂场景中包含的摄像机扫描运动、随机抖动和目标运动

提出一种基于帧间可变块差分的运动目标检测算法。首先

利用全局特征点估计运动参数对帧间背景进行补偿

提取图像的全局特征点并匹配

以特征点集的最小位置误差和作为目标进行迭代

获取误差不大于0.5 pixel的全局运动参数

并精确补偿当前帧实现背景校正。然后

利用可变块差分实现运动目标的检测。先用大尺寸对差分图像进行分割

将整幅图像粗略区分为背景区域、运动目标区域和边界区域

通过阈值判定来进一步细分

最后对运动目标区域进行准确标定。这种由粗到细的检测步骤

在保证精度的同时能够提高检测速度。实验结果表明

该算法能够准确检测含摄像机扫描和抖动的复杂运动场景中的前景运动目标

且处理速度达到25 frame/s。

Abstract

A fast moving-object detection algorithm based on inter-frame Variant Compensated Blocks Difference(VCBD) is presented to deal with the complex scenes with camera scan

dithering and object moving. Firstly

the global motion estimation is performed based on feature points to compensate the inter-frame background. The global feature points in a reference image are selected and matched in a current image. Then

the iteration is applied to realize the minimum sum of position errors of all matched points and to obtain the global motion parameter with the accuracy less than 0.5 pixel. Accordingly

the current frame is compensated to match the background area. Finally

the adaptive variant block difference is proposed to detect moving objects. The whole image is classified into background

foreground and boundary areas and the block is then judged with the threshold and divided into four blocks. These coarse-to-fine steps can greatly improve the velocity and veracity of detection. Experimental results show that the algorithm can detect moving objects in camera scan and dithering sequences and the processing speed achieves 25 frame/s.

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Keywords

references

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