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一种基于动态感知模型的异常发现方法
  • 摘要

    针对视频中异常目标行为特征的有效表示问题,提出一种基于动态感知模型的异常目标发现方法.对视频场景中的光滑、纹理、边沿区域建立动态感知模型,得到运动注意块作为候选检测位置,减少了对非感兴趣区域的冗余计算,再提取运动注意块的时空HNF特征使用稀疏编码算法训练生成字典.根据样本关于字典的重构误差是否超过预设阈值作为个体异常发现的判别标准.实验结果与测试数据库Ground Truth比较说明了该方法的有效性和实用性,且易于实现.

  • 作者

    谢锦生  郭立  李竞竞  XIE Jin-sheng  GUO Li  LI Jing-jing 

  • 作者单位

    中国科学技术大学电子科学与技术系,合肥230027;中国电子科技集团公司第三十八研究所数字技术部,合肥230088/中国科学技术大学电子科学与技术系,合肥,230027/安徽电信器材贸易工业有限公司,合肥,230011

  • 刊期

    2014年8期 ISTIC PKU

  • 关键词

    稀疏表示  视频分析  动态感知  异常发现  sparse representation  video analysis  motion perceptions  anomaly discovery 

参考文献
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