|Table of Contents|

Adaptive abnormal event detection algorithm based on sparse representation and low rank approximation

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2016年06期
Page:
666-
Research Field:
Publishing date:

Info

Title:
Adaptive abnormal event detection algorithm based on sparse representation and low rank approximation
Author(s):
Zhou Xiaoyu12Yu Bosi3Ding Enjie1
1.Research Center of Internet of Things(Sensory Mine),China University of Mining and Technology, Xuzhou 221008,China; 2.Faculty of Mechanical Electronic and Information Engineering,Jiangsu Vocational College of Finance and Economics,Huai'an 223003,China; 3.School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China
Keywords:
sparse representation low rank approximation abnormal event detection low rank sparse coding model dictionary learning K-means clustering
PACS:
TP391.4
DOI:
10.14177/j.cnki.32-1397n.2016.40.06.005
Abstract:
A dictionary learning algorithm based on a low rank sparse coding model is proposed aiming at the problem that traditional abnormal event detection algorithm doesn't consider the low rank characteristic of video sequences.Multi scale gradient characteristics of three-dimensional space-time are extracted and clustered using K-means clustering.Dictionary learning of every feature clustering is carried out using the low rank sparse coding model.Every normal behavior pattern is obtained using iteration clustering and dictionary learning.The performance of this algorithm is tested using two public data sets UCSD Ped1 and Avenue.Compared with social force(SF),mixture of probabilistic principal component analyzers(MPPCA),SF-MPPCA,mixture of dynamic texture(MDT),Adam,subspace and sparse combination learning framework(SCLF),the result of this algorithm is more correct and real-time.

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Last Update: 2016-12-30