The detection of abnormal vehicle events is a research hotspot in the analysis of highway surveillance video.Because of the complex factors,which include different conditions of weather,illumination,noise and so on,vehicle's feature extraction and abnormity detection become difficult.This paper proposes a Fast Constrained Delaunay Triangulation(FCDT) algorithm to replace complicated segmentation algorithms for multi-feature extraction.Based on the video frames segmented by FCDT,an improved algorithm is presented to estimate background self-adaptively.After the estimation,a multi-feature eigenvector is generated by Principal Component Analysis(PCA) in accordance with the static and motional features extracted through locating and tracking each vehicle.For abnormity detection,adaptive detection modeling of vehicle events(ADMVE) is presented,for which a semi-supervised Mixture of Gaussian Hidden Markov Model(MGHMM) is trained with the multi-feature eigenvectors from each video segment.The normal model is developed by supervised mode with manual labeling,and becomes more accurate via iterated adaptation.The abnormal models are trained through the adapted Bayesian learning with unsupervised mode.The paper also presents experiments using real video sequence to verify the proposed method.
SHENG Hao1,XIONG Zhang1,WENG JingNong2 & WEI Qi1 1 College of Computer,Beihang University,Beijing 100191,China
Internet资源的指数级增长促进了个性化服务的发展.针对传统的用户兴趣建模方法在准确率和增量处理能力方面的不足,提出了一种新的基于概念聚类的用户兴趣建模方法UIM2C2(User Interest Modeling Method based on Conceptual Clustering).该方法首先通过分析用户访问的历史文档构造后缀树结构,然后选择不同的相似度阈值,以不同的粒度合并基本簇.依据不同阈值条件下合并的基本簇之间的包含关系,生成用户的兴趣层次.UIM2C2方法是针对文档的一个增量式、无监督的概念学习方法,因此用户描述文件可以轻易的获取和更新.最后,通过数据集20NewsGroup上的实验验证了UIM2C2方法在兴趣预测方面的有效性.
