南京理工大学学报（自然科学版）2016，Vol.40Issue(6)：666-673,8.DOI:10.14177/j.cnki.32-1397n.2016.40.06.005
基于稀疏表示和低秩逼近的自适应异常事件检测算法
Adaptive abnormal event detection algorithm based on sparse representation and low rank approximation
摘要
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 <br> 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.关键词
稀疏表示/低秩逼近/异常事件检测/低秩稀疏编码模型/字典学习/K-均值聚类Key words
sparse representation/low rank approximation/abnormal event detection/low rank sparse coding model/dictionary learning/K-means clustering分类
信息技术与安全科学引用本文复制引用
周晓雨,余博思,丁恩杰..基于稀疏表示和低秩逼近的自适应异常事件检测算法[J].南京理工大学学报（自然科学版）,2016,40(6):666-673,8.基金项目
淮安市科技支撑计划(HAS2014023) （HAS2014023）
