Applying bayes Markov chains for the detection of ATM related scenarios

Dejan Arsić, Atanas Lyutskanov, Moritz Kaiser, Björn Schuller, Gerhard Rigoll

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

4 Scopus citations

Abstract

Video surveillance systems have been introduced in various fields of our daily life to enhance security and protect individuals and sensitive infrastructure. Up to now it has been usually utilized as a forensic tool for after the fact investigations and are commonly monitored by human operators. In order to assist these and to be able to react in time, a fully automated system is desired. In this work we will present a multi camera surveillance system, which is required to resolve heavy occlusions, to detect robberies at ATM machines. The resulting trajectories will be analyzed for so called Low Level Activities (LLA), such as walking, running and stationarity, applying simple but robust approaches. The results of the LLA analysis will subsequently be fed into a Bayesian Network, that is used as a stochastic model to model so called High Level Activities (HLA). Introducing state transitions between HLAs will allow a temporal modeling of a complex scene. This can be represented by a Markovian process.

Original languageEnglish
Title of host publication2009 Workshop on Applications of Computer Vision, WACV 2009
DOIs
StatePublished - 2009
Externally publishedYes
Event2009 Workshop on Applications of Computer Vision, WACV 2009 - Snowbird, UT, United States
Duration: 7 Dec 20098 Dec 2009

Publication series

Name2009 Workshop on Applications of Computer Vision, WACV 2009

Conference

Conference2009 Workshop on Applications of Computer Vision, WACV 2009
Country/TerritoryUnited States
CitySnowbird, UT
Period7/12/098/12/09

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