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An Online HDP Mixture Model for Video Mining

Volume 14, Number 11, November 2018, pp. 2864-2876
DOI: 10.23940/ijpe.18.11.p32.28642876

Lin Tanga, Lin Liub, Mingjing Tangc, and Yu Sunb

aKey Laboratory of Educational Informatization for Nationalities Ministry of Education, Yunnan Normal University, Kunming, 650500, China
bSchool of Information, Yunnan Normal University, Kunming, 650500, China
cPresident Office, Yunnan Normal University, Kunming, 650500, China

(Submitted on August 4, 2018; Revised on September 6, 2018; Accepted on October 6, 2018)


In this paper, we address two problems in video mining: real-time inference and the automatic decision of the number of activities in videos. To solve these problems, we present a real-time Bayesian non-parametric model that is able to discover activities and interactions of videos in real-time. In this model, there are two layers modeled in each scene, which are activities and interactions. An activity is represented as the distribution over visual words, and an interaction is represented as the distribution over activities. Then, the Hierarchical Dirichlet Process (HDP) model connects these two layers of video and automatically decides the number of clusters. Moreover, we developed a hybrid stochastic variational Gibbs sampling algorithm for inferring the parameters of the HDP mixture model. This online inference algorithm has the capacity to process the massive video stream dataset. Finally, the detailed experimental results in a crowded traffic scene and a simulated dataset are described and reveal that our online HDP mixture model achieves superior performance in real-time anomaly activity detection.


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