A Vehicle Occupant Counting System Based on Near-Infrared Phenomenology and Fuzzy Neural Classification

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30 Scopus citations


We undertook a study to determine if the automatic detection and counting of vehicle occupants is feasible. An automated vehicle occupant counting system would greatly facilitate the operation of freeway lanes reserved for buses, car-pools, and emergency vehicles (HOV lanes). In the present paper, we report our findings regarding the appropriate sensor phenomenology and arrangement for the task. We propose a novel system based on fusion of near-infrared imaging signals and we demonstrate its adequacy with theoretical and experimental arguments. We also propose a fuzzy neural network classifier to operate upon the fused near-infrared imagery and perform the occupant detection and counting function. We demonstrate experimentally that the combination of fused near-infrared phenomenology and fuzzy neural classification produces a robust solution to the problem of automatic vehicle occupant counting. We substantiate our argument by providing comparative experimental results for vehicle occupant counters based on visible, single near-infrared, and fused near-infrared bands. Interestingly, our proposed solution can find a more general applicability as the basis for a reliable face detector both indoors and outdoors.

Original languageEnglish (US)
Pages (from-to)72-84
Number of pages13
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number2
StatePublished - Jun 2000

Bibliographical note

Funding Information:
Manuscript received March 6, 2000; revised August 29, 2000. This work was supported by the Minnesota Department of Transportation under Contract Q5216211101.The Guest Editor for this paper was Dr. Katsushi Ikeuchi. I. Pavlidis and V. Morellas are with the Honeywell Technology Center, Minneapolis, MN 55418 USA (e-mail: ioannis.pavlidis@honeywell.com; vassilios. morellas@honeywell.com). N. Papanikolopoulos is with the Department of Computer Science, University of Minnesota, Minneapolis, MN 55455 USA (e-mail: npapas@cs.umn.edu). Publisher Item Identifier S 1524-9050(00)10229-7.


  • Fuzzy neural network
  • Near-infrared fusion
  • Vehicle occupant detection


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