计算机科学 ›› 2019, Vol. 46 ›› Issue (6): 311-315.doi: 10.11896/j.issn.1002-137X.2019.06.047
祝轩, 王磊, 张超, 梅东锋, 薛珈萍, 曹晴雯
ZHU Xuan, WANG Lei, ZHANG Chao, MEI Dong-feng, XUE Jia-ping, CAO Qing-wen
摘要： 运动目标检测是机器视觉领域中的关键技术之一,其在视频运动目标检测、遥感信息处理和军事侦察等领域有广泛的应用。考虑到视频中相邻视频帧背景相似性高且时间连续性长,而阴影和噪声具有非连续性的特征,文中提出一种时间连续性约束的低秩分解背景更新模型,并将其应用于背景模型减除的视频运动目标检测。该方法首先对视频进行低秩分解,获得低秩分量和稀疏分量;然后基于连续性约束背景更新模型更新低秩分量,构建背景;最后通过背景减除及自适应阈值分割获得运动目标。实验结果表明,无论是FM指标还是ROC曲线都反映出所提方法相比目前较好的背景减除方法能够有效克服阴影和噪声的影响,避免“空洞”,更准确地提取运动目标,且鲁棒性好。
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