[1]徐志通,骆炎民,柳培忠.联合加权重构轨迹与直方图熵的异常行为检测[J].智能系统学报,2018,13(6):1015-1026.[doi:10.11992/tis.201706070]
XU Zhitong,LUO Yanmin,LIU Peizhong.Abnormal behavior detection of joint weighted reconstruction trajectory and histogram entropy[J].CAAI Transactions on Intelligent Systems,2018,13(6):1015-1026.[doi:10.11992/tis.201706070]
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XU Zhitong,LUO Yanmin,LIU Peizhong.Abnormal behavior detection of joint weighted reconstruction trajectory and histogram entropy[J].CAAI Transactions on Intelligent Systems,2018,13(6):1015-1026.[doi:10.11992/tis.201706070]
联合加权重构轨迹与直方图熵的异常行为检测
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
13
期数:
2018年第6期
页码:
1015-1026
栏目:
学术论文—机器学习
出版日期:
2018-10-25
- Title:
- Abnormal behavior detection of joint weighted reconstruction trajectory and histogram entropy
- Keywords:
- abnormal behavior detection; jointly weighted; reconstruction trajectory; histogram entropy; Kalman filters; HOG features; spatio-temporal context; behavior directory
- 分类号:
- TP391.41
- 摘要:
- 为解决多目标打斗、抢劫等异常行为检测精度不高的问题，提出一种联合加权重构轨迹与直方图熵的异常行为检测算法。首先，采用背景相减法结合宽高比提取行人目标；然后将卡尔曼滤波器及HOG特征融入时空上下文算法中，实现短时间内被完全遮挡行人的鲁棒跟踪；最后对跟踪轨迹进行训练，构造正常行为字典并稀疏重构待检测轨迹，通过联合加权最小重构残差和直方图熵，实现对异常行为的有效检测。通过对比实验，表明该算法对于打斗和抢劫等异常行为具有较好的检测效果，在静态背景且无遮挡的情况下，检测率可达92%以上。
- Abstract:
- To solve the problem of low precision in detecting multi-target abnormal behaviors such as fighting and robbery, in this paper, a novel human abnormal behavior detection algorithm is proposed based on a combination of the weighted reconstruction trajectory and histogram entropy. First, the background subtraction method is combined with aspect ratio to extract pedestrian targets, and then Kalman filters and histogram of oriented gradient (HOG) features are integrated into a spatio-temporal context algorithm to realize a robust tracking of completely occluded pedestrians in a short time, and finally the tracked trajectories are trained; a normal behavior directory is constructed for the motion trajectories defined as normal, and the trajectories to be tested are sparsely reconstructed. An effective detection of abnormal behavior is realized by combining the weighted minimum reconstruction residuals and amplitude direction histogram entropy. A comparative experiment shows that the algorithm can effectively detect abnormal behaviors such as fighting and robbery, and the detection rate can exceed 92% under static background without occlusions.
- 参考文献/References:
-
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备注/Memo
收稿日期:2017-06-22。
基金项目:国家自然科学基金项目（61605048）；福建省自然科学基金项目（14BS215）；泉州市科技计划项目（2015Z120）.
作者简介:徐志通,男,1993年生,硕士研究生,主要研究方向为图像处理、计算机视觉、行人异常行为分析;骆炎民,男,1974年生,副教授,博士,日本筑波大学高级访问学者,主要研究方向为人工智能、机器学习、图像处理、数据挖掘。发表学术论文16篇,其中被SCI或EI检索9篇,主持及参与科研项目8项;柳培忠,男,1976年生,讲师,博士,美国杜克大学高级访问学者,主要研究方向为仿生智能计算、仿生图像处理技术、多维空间仿生信息学。主持及参与课题项目6项。发表学术论文15篇。
通讯作者:骆炎民.E-mail:lym@hqu.edu.cn
更新日期/Last Update:
2018-12-25