A robust person detector for overhead views

Imran Ahmed, John N. Carter

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

19 Citations (Scopus)

Abstract

In cluttered environments the overhead view is often preferred because looking down can afford better visibility and coverage. However detecting people in this or any other extreme view can be challenging as there is a significant variation in a person's appearances depending only on their position in the picture. The Histogram of Oriented Gradient (HOG) algorithm, a standard algorithm for pedestrian detection, does not perform well here, especially where the image quality is poor. We show that on average, 9 false detections occur per image. We propose a new algorithm where transforming the image patch containing a person to remove positional dependency and then applying the HOG algorithm eliminates 98% of the spurious detections in noisy images from an industrial assembly line and detects people with a 95% efficiency.

Original languageEnglish
Title of host publicationICPR 2012 - 21st International Conference on Pattern Recognition
Pages1483-1486
Number of pages4
Publication statusPublished - 2012
Externally publishedYes
Event21st International Conference on Pattern Recognition, ICPR 2012 - Tsukuba, Japan
Duration: 11 Nov 201215 Nov 2012

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Conference

Conference21st International Conference on Pattern Recognition, ICPR 2012
Country/TerritoryJapan
CityTsukuba
Period11/11/1215/11/12

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