复杂背景下基于LBP纹理特征的运动目标快速检测算法
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作者单位:

1.中国科学院上海技术物理研究所,上海 200083;2.中国科学院大学,北京 100049;3.中国科学院红外探测与成像技术重点实验室,上海 200083

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基金项目:

中国科学院青年创新促进会资助


Fast moving target detection algorithm based on LBP texture feature in complex background
Author:
Affiliation:

1.Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China;2.University of Chinese Academy of Sciences, Beijing 10049, China;3.Key Laboratory of Infrared System Detection and Imaging Technology, Chinese Academy of Sciences, Shanghai 200083, China

Fund Project:

Supported by the Youth Innovation Promotion Association CAS

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    摘要:

    在雨雪天气、树叶晃动、水面闪烁等有复杂背景的可见光与红外场景中,快速准确地提取完整目标一直是运动目标检测中的首要难题。为了满足实时性,并针对现有视频的前景提取算法依赖先验信息、召回率低、缺乏纹理和噪声较大等问题,提出了一种基于直方图统计和改进的LBP(Local Binary Pattern)纹理特征相结合的背景建模方法。首先,使用各像素直方图的众数作为参考背景无需先验知识,节省了大量存储空间,再采用邻域补偿策略提出了一种改进的S_MBLBP纹理直方图与参考背景进行背景建模,消除了大部分动态背景和光照变化影响,实现目标的精确提取。实验表明,所提的算法在红外和可见光的多种复杂场景下,能快速提取前景目标的同时,提高了的准确率和召回率。

    Abstract:

    In the visible and infrared scenes with complex background, such as rain and snow weather, leaf swaying, shimmering water, etc., fast and accurate extraction of a complete target has always been the primary problem in moving target detection. In order to be real time and aiming at the problems of existing video foreground extraction algorithms, such as dependence on prior information, low recall rate, lack of texture and large noise, a background modeling method based on histogram statistics and improved LBP (Local Binary Pattern) texture features is proposed. Firstly, the mode of each pixel histogram is used as the reference background without prior knowledge, which saves a lot of storage space. Then, an improved S_MBLBP texture histogram is proposed to model the background with the reference background by using neighborhood compensation strategy, which eliminates the most dynamic background and illumination changes, and realizes the accurate extraction of the target. Experimental results show that the proposed algorithm can quickly extract foreground targets in a variety of complex infrared and visible scenes, and can improve the accuracy and recall rate at the same time.

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历史
  • 收稿日期:2021-07-23
  • 最后修改日期:2022-03-03
  • 录用日期:2021-09-07
  • 在线发布日期: 2022-02-28
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