Pedestrian detection based on blob motion statistics

Borges, Paulo Vinicius Koerich (2013) Pedestrian detection based on blob motion statistics. IEEE Transactions on Circuits and Systems for Video Technology, 23 2: 224-235. doi:10.1109/TCSVT.2012.2203217

Author Borges, Paulo Vinicius Koerich
Title Pedestrian detection based on blob motion statistics
Journal name IEEE Transactions on Circuits and Systems for Video Technology   Check publisher's open access policy
ISSN 1051-8215
Publication date 2013-02-01
Year available 2013
Sub-type Article (original research)
DOI 10.1109/TCSVT.2012.2203217
Volume 23
Issue 2
Start page 224
End page 235
Total pages 12
Place of publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Language eng
Abstract Pedestrian detection based on video analysis is a key functionality in automated surveillance systems. In this paper, we present efficient detection metrics that consider the fact that human movement presents distinctive motion patterns. Contrary to several methods that perform an intrablob analysis based on motion masks, we approach the problem without necessarily considering the periodic pixel motion inside the blob. As such, we do not analyze periodicity in the pixel luminances, but in the motion statistics of the tracked blob as a whole. For this, we propose the use of the following cues: 1) a cyclic behavior in the blob trajectory, and 2) an in-phase relationship between the change in blob size and position. In addition, we also exploit the relationship between blob size and vertical position, assuming that the camera is positioned sufficiently high. If the homography between the camera and the ground is known, the features are normalized by transforming the blob size to the real person size. For improved performance, we combine these features using the Bayes classifier. We also present a theoretical statistical analysis to evaluate the system performance in the presence of noise. We perform online experiments in a real industrial scenario and also with videos from well-known databases. The results illustrate the applicability of the proposed features.
Keyword Computer vision
Pedestrian detection
Video surveillance
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2014 Collection
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 7 times in Thomson Reuters Web of Science Article | Citations
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