计算机科学 ›› 2021, Vol. 48 ›› Issue (7): 199-205.doi: 10.11896/jsjkx.200800146
侯春萍, 赵春月, 王致芃
HOU Chun-ping, ZHAO Chun-yue, WANG Zhi-peng
摘要： 视频异常检测算法是视频处理领域的研究热点之一,用于检测视频中是否包含异常事件。然而,由于没有异常样本参与训练过程,且异常样本与正常样本之间存在一定程度的相似性,因此很难设计出一种有辨识力的异常检测模型。为了解决上述问题,文中首先提出了一种基于相似度保持和样本恢复的特征选择方法,该方法能够保留正常样本的相似关系,进而可以学习到能够准确描述正常事件的特征。其次,将异常事件检测任务转化为分类任务,并提出了一种自反馈最优子类挖掘方法来获得最优分类器。如果一个测试样本被所有分类器判断为异常,则该样本最终将被判定为异常。在公共视频数据集(Avenue数据集、UCSD Ped2数据集)上进行的大量实验的结果表明,所提异常事件检测算法可以取得很好的结果。
中图分类号:
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