Unsupervised Discovery of Activities and Their Temporal Behaviour

This paper addresses the problem of discovering activities and their temporal significance in surveillance videos in an unsupervised manner. We propose a generative model that can jointly capture the activities and their behaviour over time. We use multinomial distribution over local motion features...

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Bibliographic Details
Published in2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance pp. 100 - 105
Main Authors Faruquie, T. A., Banerjee, S., Kalra, P. K.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2012
Subjects
Computational modeling
Hidden Markov models
Surveillance
Training
Vehicles
Visualization
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Summary:This paper addresses the problem of discovering activities and their temporal significance in surveillance videos in an unsupervised manner. We propose a generative model that can jointly capture the activities and their behaviour over time. We use multinomial distribution over local motion features to model activities and a mixture distribution over their time stamps to capture the multi-modal temporal distribution of these activities. We give a Gibbs sampling algorithm to infer the parameters of the model. We demonstrate the effectiveness of our approach on real life surveillance feed of outdoor scenes.
ISBN:146732499X
9781467324991
DOI:10.1109/AVSS.2012.79