Machine Vision and Applications

, Volume 15, Issue 1, pp 29–45 | Cite as

DETER: Detection of events for threat evaluation and recognition

  • V. MorellasEmail author
  • I. Pavlidis
  • P. Tsiamyrtzis


The current security infrastructure can be summarized as follows: (1) Security systems act locally and do not cooperate in an effective manner, (2) Very valuable assets are protected inadequately by antiquated technology systems and (3) Security systems rely on intensive human concentration to detect and assess threats.

In this paper we present DETER (Detection of Events for Threat Evaluation and Recognition), a research and development (R&D) project aimed to develop a high-end automated security system. DETER can be seen as an attempt to bridge the gap between current systems reporting isolated events and an automated cooperating network capable of inferring and reporting threats, a function currently being performed by humans.

The prototype DETER system is installed at the parking lot of Honeywell Laboratories (HL) in Minneapolis. The computer vision module of DETER reliably tracks pedestrians and vehicles and reports their annotated trajectories to the threat assessment module for evaluation. DETER features a systematic optical and system design that sets it apart from “toy” surveillance systems. It employs a powerful Normal mixture model at the pixel level supported by an expectation-maximization (EM) initialization, the Jeffreys divergence measure, and the method of moments. It also features a practical and accurate multicamera calibration method. The threat assessment module utilizes the computer vision information and can provide alerts for behaviors as complicated as the “hopping” of potential vehicle thieves from vehicle spot to vehicle spot.

Extensive experimental results measured during actual field operations support DETER’s exceptional characteristics. DETER has recently been successfully productized. The product-grade version of DETER monitors movements across the length of a new oil pipeline.


Object tracking Multicamera calibration Threat assessment Surveillance system Security system 


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Copyright information

© Springer-Verlag Berlin/Heidelberg 2003

Authors and Affiliations

  1. 1.Honeywell LaboratoriesMinneapolisUSA
  2. 2.Dept. of Computer ScienceUniversity of HoustonHoustonUSA
  3. 3.School of StatisticsUniversity of MinnesotaMinneapolisUSA

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