吉林大学学报(工学版) ›› 2021, Vol. 51 ›› Issue (5): 1823-1829.doi: 10.13229/j.cnki.jdxbgxb20200441
• 计算机科学与技术 • 上一篇
Chun-ping HOU(),Chun-yue ZHAO,Zhi-peng WANG,Hai-rui TIAN
摘要:
为改善大多数异常检测算法仅通过正常样本训练模型,缺乏异常样本,将会造成一定程度的误判问题,提出了一种基于有效异常样本构造的异常检测算法。通过K-means聚类算法得到代表不同类型正常事件的聚类簇,然后,基于异常事件的时序关系构造异常样本,再结合本文构造的异常样本,利用二分类支持向量机算法训练分类器,将检测任务转化为分类任务,从而提高检测准确率。本文在经典数据集(Avenue数据集)上进行了算法有效性验证,发现本文算法的检测准确度优于一些领域内的先进算法。因此,充分利用视频的时序关系进行异常样本的构造能有效提高异常检测的有效性。
中图分类号:
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