视频监控中的人群异常行为检测与定位 | |
Alternative Title | Anomaly Detection and Location in Crowded Surveillance Videos |
周培培1,2,3,4![]() ![]() | |
Department | 光电信息技术研究室 |
Source Publication | 光学学报
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ISSN | 0253-2239 |
2018 | |
Volume | 38Issue:8Pages:1-9 |
Indexed By | EI ; CSCD |
EI Accession number | 20184105932735 |
CSCD ID | CSCD:6305054 |
Contribution Rank | 1 |
Funding Organization | 中国科学院国防科技创新基金(Y6A4160401) |
Keyword | 模式识别 人群异常检测 运动区域分割 特征提取 一类分类器 运动连续性滤波 |
Abstract | 人群中的异常行为是一大潜在威胁,自动检测监控中的异常行为成为近年的研究热点之一。然而,由于异常的未知性与复杂性,已有的检测方法仍然存在检测率低、定位精度差的问题。为此,提出了对视频监控中的人群异常行为自动检测与定位的方法。第一,结合灰度值与光流场的分布提取运动区域。第二,对运动区域分割得到有效的运动块,从中提取表示外观和动态的两种特征,即局部HOG和局部HOF特征。第三,使用k-means方法对运动块进行聚类,对每类样本使用一类分类器进行建模。最后,加入运动连续性约束,以抑制干扰噪声。在两个复杂的异常行为数据集上的实验结果表明,提出的方法明显优于已有的检测方法,且可以满足正确率高、抗干扰能力强等实际工程需求。 |
Other Abstract | The anomaly in the crowd is a great potential threat, and the automatic detection of abnormal behavior for surveillance has become a hot topic in recent years. However, because the anomaly is unknown and complex, the previous detection methods still suffer from a low detection rate and poor location accuracy. To this end, a method is proposed for anomaly detection and location in the crowded surveillance videos. First, the motion regions are extracted according to the distributions of the gray-scale value and the optical flow field. Second, the effective motion blocks are obtained by segmenting the motion regions. Two features, namely the local HOG and the local HOF, are extracted from the motion blocks, representing the appearance and dynamics. Third, the motion blocks are clustered with the k-means method, and each cluster is modeled using a one-class classifiers. Finally, the motion continuity constraint is added to suppress the noisy noises. Experimental results on two complex abnormal behavior datasets show that the proposed method is obviously better than previous detection methods. It could meet the practical engineering needs such as high accuracy and strong anti-interference ability. |
Language | 中文 |
Citation statistics | |
Document Type | 期刊论文 |
Identifier | http://ir.sia.cn/handle/173321/22091 |
Collection | 光电信息技术研究室 |
Corresponding Author | 周培培 |
Affiliation | 1.中国科学院沈阳自动化研究所; 2.中国科学院大学; 3.中国科学院光电信息处理重点实验室; 4.辽宁省图像理解与视觉计算重点实验室; 5.航天恒星科技有限公司 |
Recommended Citation GB/T 7714 | 周培培,丁庆海,罗海波,等. 视频监控中的人群异常行为检测与定位[J]. 光学学报,2018,38(8):1-9. |
APA | 周培培,丁庆海,罗海波,&侯幸林.(2018).视频监控中的人群异常行为检测与定位.光学学报,38(8),1-9. |
MLA | 周培培,et al."视频监控中的人群异常行为检测与定位".光学学报 38.8(2018):1-9. |
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视频监控中的人群异常行为检测与定位.pd(1742KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | View Application Full Text |
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