Moving object detection in combination of CenSurE and spatial-temporal information

ZHANG Hong-ying; HU Zheng

doi:10.3788/OPE.20132109.2452

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Moving object detection in combination of CenSurE and spatial-temporal information

更新时间：2020-08-12

- Moving object detection in combination of CenSurE and spatial-temporal information
- Optics and Precision Engineering Vol. 21, Issue 9, Pages: 2452-2463(2013)
- 作者机构：
  
  中国民航大学航空自动化学院天津,300300
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20132109.2452
  CLC： TP391.4
- Received：24 January 2013，
  
  Revised：07 April 2013，
  
  Published Online：30 September 2013，
  
  Published：15 September 2013
- 稿件说明：
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张红颖胡正. CenSurE特征和时空信息相结合的运动目标检测[J]. 光学精密工程, 2013,21(9): 2452-2463

ZHANG Hong-ying HU Zheng. Moving object detection in combination of CenSurE and spatial-temporal information[J]. Editorial Office of Optics and Precision Engineering, 2013,21(9): 2452-2463
张红颖胡正. CenSurE特征和时空信息相结合的运动目标检测[J]. 光学精密工程, 2013,21(9): 2452-2463 DOI： 10.3788/OPE.20132109.2452.

ZHANG Hong-ying HU Zheng. Moving object detection in combination of CenSurE and spatial-temporal information[J]. Editorial Office of Optics and Precision Engineering, 2013,21(9): 2452-2463 DOI： 10.3788/OPE.20132109.2452.

摘要

提出一种结合中心环绕极值特征(CenSurE)和时空信息的运动目标检测算法，用于提高动态场景中运动目标检测的速度和目标的完整性。首先，根据CenSurE特征点提取的快速性和精确性，使用该特征和单应性变换模型快速、准确地配准运动序列帧间图像，从而补偿摄像机运动引起帧间背景的平移、旋转和缩放量。然后，在时域对背景配准帧用帧差信息生成运动前景掩模，根据前景掩模的空域信息建立动态更新的实时背景，并使用空域背景减除和一种基于概率统计的自适应阈值分割方法提取较为完整的前景运动目标。最后，通过标准视频序列进行测试以验证算法的有效性。实验结果表明，该算法能够达到15 frame/s的处理速度，且在保证检测速度的同时可得到完整的运动目标，基本满足动态场景中运动目标检测的快速性、抗噪性、光照适应性以及目标完整性等指标。

Abstract

An algorithm combining Center Surround Extremas(CenSurE) and spatial-temporal information was proposed to improve the detection speed and detection integrity for a moving object in dynamic scenes. Firstly

in terms of the rapidity and accuracy of CenSurE feature point extraction

an inter-frame image registration method for the motion sequence base on CenSurE and a homography transformation model was proposed to compensate the translation factors

rotation angles and scaling coefficients caused by a moving camera. Then

the foreground mask was generated according to the temporal inter-frame difference of the registrated background in a time domain. Moreover

a dynamical updating background was established with spatial information of the foreground mask and a complete moving object was extracted through background subtraction and a self-adaptive threshold segmentation method based on probability and statics. Finally

the experiments on several standard video sequences were tested to evaluate the performance of this algorithm. Experiment results show that the operating speed of this proposed algorithm can be 15 frame/s

meanwhile

complete moving objects can be obtained with high detecton speeds. The proposed algorithm basically meets the demands of moving object detection in dynamic scenes for speeds

noise resistances

light adaptability

object integrity and so on.

关键词

Keywords

references

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