IPSJ Online Transactions
Online ISSN : 1882-6660
ISSN-L : 1882-6660
Detection of Activities and Events without Explicit Categorization
Masao YamanakaMasakazu MatsuguMasashi Sugiyama
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JOURNALS FREE ACCESS

2013 Volume 6 Pages 104-110

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Abstract

We propose a method of unsupervised event detection from a video that compares probability distributions of past and current video sequence data in a sequential and hierarchical way. Because estimation of probability distributions is known to be difficult, naively comparing probability distributions via probability distribution estimation tends to be unreliable in practice. To cope with this problem, we use the state-of-the-art machine learning technique called density ratio estimation: The ratio of probability densities is directly estimated without density estimation, and thus probability distributions can be compared in a reliable way. Through experiments on a walking scene and a tennis match, we demonstrate the usefulness of the proposed approach.

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© 2013 by the Information Processing Society of Japan
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