Detecting Interesting Events Using Unsupervised Density Ratio Estimation

Yuichi Ito¹, Kris M. Kitani², James A. Bagnell², and Martial Hebert²

¹Nikon Corporation, Shinagawa, Tokyo 140-8601, Japan

²Carnegie Mellon University, Pittsburgh, PA 15213, USA

Abstract. Generating meaningful digests of videos by extracting interesting frames remains a difficult task. In this paper, we define interesting events as unusual events which occur rarely in the entire video and we propose a novel interesting event summarization framework based on the technique of density ratio estimation recently introduced in machine learning. Our proposed framework is unsupervised and it can be applied to general video sources, including videos from moving cameras. We evaluated the proposed approach on a publicly available dataset in the context of anomalous crowd behavior and with a challenging personal video dataset. We demonstrated competitive performance both in accuracy relative to human annotation and computation time.

Keywords: Video Summarization, Density Ratio Estimation

LNCS 7585, p. 151 ff.

Full article in PDF | BibTeX

lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2012