激光与光电子学进展, 2020, 57 (24): 241507, 网络出版: 2020-12-01  

雨雪天气条件下的运动目标检测 下载: 1068次

Moving Object Detection Under Rain and Snow Weather Conditions
作者单位
江西理工大学电气工程与自动化学院, 江西 赣州 341000
摘要
针对雨雪天气条件下的运动目标检测受到天气的影响较大,提出一种融合全变分(TV)正则化和Rank-1约束鲁棒主成分分析(RPCA)模型的视频序列运动目标检测算法。利用RPCA这一工具,在低秩稀疏分解框架下,采用Rank-1约束描述背景层的强低秩性,利用TV正则化结合L1范数对前景目标的稀疏性和空间连续性进行约束,从而弥补现有RPCA模型的不足。针对所提模型,采用交替迭代乘子法的思想结合增广拉格朗日乘子法对目标函数进行优化求解。实验结果表明,所提算法不仅能够准确检测出运动目标,而且具有较短的运行时间,这为视频的实时检测提供参考。与其他同类算法相比,所提算法不仅检测效果更佳,而且在F测度值、召回率和准确率的定量评价中均有优越性。
Abstract
In view of the fact that the detection of moving targets in real-time video are greatly affected by weather conditions. Herein, a video sequence moving target detection algorithm that combines total variation(TV) regularization and a Rank-1 constrained robust principal component analysis (RPCA) model is proposed. Using RPCA as a tool in the framework of low-rank sparse decomposition, the Rank-1 constraint is used to describe the strong low-rank of the background layer, and the TV regularization combined with the L1 norm is used to perform the sparseness and spatial continuity of the foreground target constraints to compensate for the deficiencies of the existing RPCA model. Aiming at the proposed model, the idea of alternating iterative multiplier method combined with augmented Lagrangian multiplier method is used to optimize the objective function. Experimental results show that the proposed algorithm can not only accurately detect moving targets but also has a shorter running time, which provides a reference for real-time video detection. Compared with other similar algorithms, the proposed algorithm not only has better detection effect but also provides enhanced quantitative evaluation of F measurement value, recall rate, and accuracy rate.

杨国亮, 喻丁玲, 王杨, 王艳芳. 雨雪天气条件下的运动目标检测[J]. 激光与光电子学进展, 2020, 57(24): 241507. Guoliang Yang, Dingling Yu, Yang Wang, Yanfang Wang. Moving Object Detection Under Rain and Snow Weather Conditions[J]. Laser & Optoelectronics Progress, 2020, 57(24): 241507.

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