上海交通大学学报 ›› 2021, Vol. 55 ›› Issue (5): 607-614.doi: 10.16183/j.cnki.jsjtu.2020.120
所属专题: 《上海交通大学学报》2021年12期专题汇总专辑; 《上海交通大学学报》2021年“自动化技术、计算机技术”专题
收稿日期:
2020-04-26
出版日期:
2021-05-28
发布日期:
2021-06-01
作者简介:
武光利(1981-),男,山东省潍坊市人,教授,现主要从事信息内容安全、人工智能等研究.电话(Tel.):0931-7601406;E-mail: 基金资助:
WU Guangli1,2(), GUO Zhenzhou1, LI Leiting1, WANG Chengxiang1
Received:
2020-04-26
Online:
2021-05-28
Published:
2021-06-01
摘要:
针对传统视频异常检测模型的缺点,提出一种融合全卷积神经(FCN)网络和长短期记忆(LSTM)网络的网络结构.该网络结构可以进行像素级预测,并能精确定位异常区域.首先,利用卷积神经网络提取视频帧不同深度的图像特征;然后,把不同的图像特征分别输入记忆网络分析时间序列的语义信息,并通过残差结构融合图像特征和语义信息;同时,采用跳级结构集成多模态下的融合特征并进行上采样,最终获得与原视频帧大小相同的预测图.所提网络结构模型在加州大学圣地亚哥分校(UCSD)异常检测数据集的ped 2子集和明尼苏达大学(UMN)人群活动数据集上进行测试,均取得了较好的结果.在UCSD上的等错误率低至6.6%,曲线下面积达到了98.2%, F1分数达到了94.96%;在UMN上的等错误率低至7.1%,曲线下面积达到了93.7%,F1分数达到了94.46%.
中图分类号:
武光利, 郭振洲, 李雷霆, 王成祥. 融合FCN和LSTM的视频异常事件检测[J]. 上海交通大学学报, 2021, 55(5): 607-614.
WU Guangli, GUO Zhenzhou, LI Leiting, WANG Chengxiang. Video Abnormal Detection Combining FCN with LSTM[J]. Journal of Shanghai Jiao Tong University, 2021, 55(5): 607-614.
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