Abnormal event detection based on local topology and l<sub>1/2</sub>norm regularize

Qing YU; Ken CHEN; Meng LI; Fei LI

doi:10.11959/j.issn.1000-0801.2018254

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Abnormal event detection based on local topology and l_1/2norm regularize

research and development | 更新时间：2024-06-05

- Abnormal event detection based on local topology and l_1/2norm regularize
- Telecommunications Science Vol. 34, Issue 10, Pages: 65-71(2018)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2018254
  CLC： TP391
- Published Online：2018-10，
  
  Published：20 October 2018
- 稿件说明：
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Qing YU, Ken CHEN, Meng LI, et al. Abnormal event detection based on local topology and l_1/2norm regularize[J]. Telecommunications science, 2018, 34(10): 65-71.
DOI：

Qing YU, Ken CHEN, Meng LI, et al. Abnormal event detection based on local topology and l_1/2norm regularize[J]. Telecommunications science, 2018, 34(10): 65-71. DOI： 10.11959/j.issn.1000-0801.2018254.

摘要

所提方案在传统解析字典算法基础上，加入局部拓扑项用以描述数据之间的结构信息，同时用l

1/2

范数代替 l

范数作为稀疏约束，从而提高表示系数的稀疏度。在特征提取上，融合了包含丰富运动信息的相互作用力直方图与包含纹理信息的梯度方向直方图，然后用改进的字典对特征数据进行训练，最后通过计算测试样本在该字典下的重构误差来判断测试样本是否为异常样本。在标准行为库UMN（University of Minnesota）数据库上进行的实验证实了算法具有较高的性能。与传统的算法相比，提出的改进的解析字典分类算法在针对人群异常事件中取得了更为有效的检测。

Abstract

A new dictionary learning method was proposed by introducing a local topology term to describe structural information of video events and using the l

1/2

norm as the sparsity constraint to the representation coefficients based on the traditional analysis dictionary learning method.In feature extraction

a histogram of interaction force(HOIF) containing rich motion information and a histogram of oriented gradient(HOG) containing texture information were merged.Then

the improved dictionary was used to train the feature data.Finally

the reconstruction error of the testing sample under the dictionary was used to determine whether the testing sample was an abnormal sample.Experiments on UMN show the high performance of the algorithm.Compared with the state-of-the-art algorithms

the analysis dictionary classification algorithm based on local topology and l

1/2

norm has made more effective detection on the abnormal events in the crowd.

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references

ZHANG Y , LU H C , ZHANG L H , et al . Combining motion and appearance cues for anomaly detection [J ] . Pattern Recognition , 2016 , 51 ( C ): 443 - 452 .

吴新宇 , 郭会文 , 李楠楠 , 等 . 基于视频的人群异常事件检测综述 [J ] . 电子测量与仪器学报 , 2014 , 28 ( 6 ): 575 - 584 .

WU X Y , GUO H W , LI N N , et al . Survey on the video-based abnormal event dete-ction in crowd scenes [J ] . Journal of Electronic Measurement and Instrumentation , 2014 , 28 ( 6 ): 575 - 584 .

ZHAO B , LI F F , XING E P . Online detection of unusual eventsin videos via dynamic-sparse coding [J ] . Computer Vision and Pattern Recognition , 2011 , 32 ( 4 ): 3313 - 3320 .

MEHRAN R , OYAMA A , SHAH M . Abnormal crowd behavior detection using social force model [C ] // IEEE Conference on Computer Vision and Pattern Recognition,June 20-25,2009,Miami,FL,USA . Piscataway:IEEE Press , 2009 : 935 - 942 .

WU S , MOORE B E , SHAH M . Chaotic invariants of lagrangian particle trajectories for anomaly detection in crowded scenes [C ] // IEEE Conference on Computer Vision and Pattern Recognition,June 13-18,2010,San Francisco,CA,USA . Piscataway:IEEE Press , 2010 : 2054 - 2060 .

CONG Y , YUAN J , LIU J . Abnormal event detection in crowded scenes using sparse representation [J ] . Pattern Recognition , 2013 ( 46 ): 1851 - 1864 .

ZHU X , LIU J , WANG J , et al . Sparse representation for robust abnormality detection in crowded scenes [J ] . Pattern Recognition , 2014 , 47 ( 5 ): 1791 - 1799 .

ZHANG Z , MEI X , XIAO B . Abnormal event detection via compact low-rank sparse learning [J ] . Pattern Recognition , 2016 , 31 ( 2 ): 29 - 36 .

GUO J , GUO Y Q , KONG X W , et al . Discriminative analysis dictionary learning [C ] // Thirtieth AAAI Conference on Artificial Intelligence(AAAI-16),February 12-17,2016,Phoenix,Arizona,USA . Palo Alto:AAAI Press , 2016 : 1617 - 1623 .

YUAN Y , FENG Y , LU X . Structured dictionary learning for abnormal event detection in crowded scenes [J ] . Pattern Recognition , 2018 ( 73 ): 99 - 110 .

练秋生 , 石保顺 , 陈书贞 . 字典学习模型、算法及其应用研究进展 [J ] . 自动化学报 , 2015 , 41 ( 2 ): 240 - 260 .

LIAN Q S , SHI B S , CHEN S Z . Research advances on dictionary learning models,algorithms and applications [J ] . Acta Automatica Sinica , 2015 , 41 ( 2 ): 240 - 260 .

NAM S , DAVIES M E , ELAD M , et al . The cosparse analysis model and algorithm-MS [J ] . Applied ＆ Computational Harmonic Analysis , 2013 , 34 ( 1 ): 30 - 56 .

LUO D , DING C , NIE F , et al . Cauchy graph embedding [C ] // International Conference on International Conference on Machine Learning,June 28-July 2,2011,Bellevue,Washington,USA . New York:ACM Press , 2011 : 553 - 560 .

LI Z , HAYASHI T , DING S , et al . Constrained analysis dictionary learning with the ℓ1/2-norm regularizer [C ] // International Conference on Signal Processing,January 9-11,2017,Da Nang,Vietnam . Piscataway:IEEE Press , 2017 : 890 - 894 .

HE R , ZHENG W S , TAN T , et al . Half-quadratic-based iterative minimization for robust sparse representation [J ] . IEEE Transactions on Pattern Analysis＆Machine Intelligence , 2014 , 36 ( 2 ): 261 - 275 .

李萌 , 陈恳 , 郭春梅 , 等 . 融合显著性信息和社会力模型的人群异常检测 [J ] . 光电工程 , 2016 , 43 ( 12 ): 193 - 199 .

LI M , CHEN K , GUO C M , et al . Abnormal crowed event detection by fusing saliency information and social force mode [J ] . Opto-Electronic Engineering , 2016 , 43 ( 12 ): 193 - 199 .

UIJLINGS J R R , DUTA I C , ROSTAMZADEH N , et al . Realtime video classification using dense HOF/HOG [C ] // International Conference on Multimedia Retrieval,Apr 1-4,2014,Glasgow,UK . New York:ACM Press , 2014 : 145 - 152 .

YI Y G , LI X H , ZHAO R . A constrained sparse representation approach for video anomaly detection [C ] // Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC),October 3-5,2017,Xi’an,China.[S.l.:s.n] . 2017 : 45 - 49 .

LI A , MIAO Z J , CEN Y G , et al . Abnormal event detection based on sparse reconstruction in crowded scenes [C ] // 2016 IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP),March 20-25,2016,Shanghai,China . Piscataway:IEEE Press , 2016 : 1786 - 1790 .

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Abnormal event detection based on local topology and l1/2norm regularize

DOI：10.11959/j.issn.1000-0801.2018254

摘要

Abstract

关键词

Keywords

references

Abnormal event detection based on local topology and l_1/2norm regularize