Unusual behavior detection in the entry gate scenes of subway station using Bayesian networks and inference

Sooyeong Kwak, Guntae Bae, Manbae Kim, Hyeran Byun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


In this paper, we propose a method for detecting unusual human behavior using monocular camera which is not moving. Our system composed of three modules which are moving object detection, tracking, and event recognition. The key part is event recognition module. We define unusual events which are composed of two simple events (drop off luggage, unattended luggage) and two complex events (abandoned luggage and steal luggage). In order to detect the simple event, we construct Bayesian network in each unusual event. We extract evidences using bounding box properties which are the location of moving objects, speed, distance between the person and the other moving object (such as bag), existing time. And then, we use finite state automaton which shows the temporal relation of two simple events to detect complex events. To evaluate the performance, we compare the frame number when an even is triggered with our results and the ground truth. The proposed algorithm showed good results on the real world environment and also worked at real time speed.

Original languageEnglish
Title of host publicationImage Processing
Subtitle of host publicationMachine Vision Applications
Publication statusPublished - 2008
EventImage Processing: Machine Vision Applications - San Jose, CA, United States
Duration: 2008 Jan 292008 Jan 31

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


OtherImage Processing: Machine Vision Applications
Country/TerritoryUnited States
CitySan Jose, CA

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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