Identifying surprising events in video using bayesian topic models

Avishai Hendel*, Daphna Weinshall, Shmuel Peleg

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


In this paper we focus on the problem of identifying interesting parts of the video. To this end we employ the notion of Bayesian surprise, as defined in [9, 10], in which an event is considered surprising if its occurrence leads to a large change in the probability of the world model. We propose to compute this abstract measure of surprise by first modeling a corpus of video events using the Latent Dirichlet Allocation model. Subsequently, we measure the change in the Dirichlet prior of the LDA model as a result of each video event's occurrence. This leads to a closed form expression for an event's level of surprise. We tested our algorithm on a real world video data, taken by a camera observing an urban street intersection. The results demonstrate our ability to detect atypical events, such as a car making a U-turn or a person crossing an intersection diagonally.

Original languageAmerican English
Title of host publicationDetection and Identification of Rare Audiovisual Cues
EditorsDaphna Weinshall, Jorn Anemuller, Luc Gool, Luc Gool
Number of pages9
StatePublished - 2012

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X

Bibliographical note

Funding Information:
Work is funded by the EU Integrated Project DIRAC (IST-027787).


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