NDLI: A combination of generative and discriminative models for fast unsupervised activity recognition from traffic scene videos

Content Provider	IEEE Xplore Digital Library
Author	Krishna, M.V. Denzler, J.
Copyright Year	2014
Description	Author affiliation: Comput. Vision Group, Friedrich Schiller Univ. Jena, Jena, Germany (Krishna, M.V.; Denzler, J.)
Abstract	Recent approaches in traffic and crowd scene analysis make extensive use of non-parametric hierarchical Bayesian models for intelligent clustering of features into activities. Although this has yielded impressive results, it requires the use of time consuming Bayesian inference during both training and classification. Therefore, we seek to limit Bayesian inference to the training stage, where unsupervised clustering is performed to extract semantically meaningful activities from the scene. In the testing stage, we use discriminative classifiers, taking advantage of their relative simplicity and fast inference. Experiments on publicly available data-sets show that our approach is comparable in classification accuracy to state-of-the-art methods and provides a significant speed-up in the testing phase.
Sponsorship	IEEE Comput. Soc.
Starting Page	640
Ending Page	645
File Size	6558454
Page Count	6
File Format	PDF
ISBN	9781479949854
DOI	10.1109/WACV.2014.6836042
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-03-24
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Videos Junctions Training Bayes methods Testing Computational modeling Accuracy
Content Type	Text
Resource Type	Article

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