Detection of loitering individuals in public transportation areas

Nathaniel D. Bird, Osama Masoud, Nikolaos P. Papanikolopoulos, Aaron Isaacs

Research output: Contribution to journalArticlepeer-review

106 Scopus citations

Abstract

This paper presents a vision-based method to automatically detect individuals loitering about inner-city bus stops. Using a stationary camera view of a bus stop, pedestrians are segmented and tracked throughout the scene. The system takes snapshots of individuals when a clean, nonobstructed view of a pedestrian is found. The snapshots are then used to classify the individual images into a database, using an appearance-based method. The features used to correlate individual images are based on short-term biometrics, which are changeable but stay valid for short periods of time; this system uses clothing color. A linear discriminant method is applied to the color information to enhance the differences and minimize similarities between the different individuals in the feature space. To determine if a given individual is loitering, time stamps collected with the snapshots in their corresponding database class can be used to judge how long an individual has been present. An experiment was performed using a 30-min video of a busy bus stop with six individuals loitering about it. Results show that the system successfully classifies images of all six individuals as loitering.

Original languageEnglish (US)
Pages (from-to)167-177
Number of pages11
JournalIEEE Transactions on Intelligent Transportation Systems
Volume6
Issue number2
DOIs
StatePublished - Jun 2005

Bibliographical note

Funding Information:
Manuscript received September 24, 2004; revised January 27, 2005. This work was supported in part by the National Science Foundation through grant #IIS-0219863, the Minnesota Department of Transportation, and the ITS Institute at the University of Minnesota.

Keywords

  • Computer vision
  • Human activities recognition
  • Short-term biometrics
  • Surveillance

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