Simple real-time human detection using a single correlation filter

David S. Bolme, Yui Man Lui, Bruce A. Draper, J. Ross Beveridge

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

35 Scopus citations

Abstract

This paper presents an extremely simple human detection algorithm based on correlating edge magnitude images with a filter. The key is the technology used to train the filter: Average of Synthetic Exact Filters (ASEF). The ASEF based detector can process images at over 25 frames per second and achieves a 94.5% detection rate with less than one false detection per frame for sparse crowds. Filter training is also fast, taking only 12 seconds to train the detector on 32 manually annotated images. Evaluation is performed on the PETS 2009 dataset and results are compared to the OpenCV cascade classifier and a state-of-the-art deformable parts based person detector.

Original languageEnglish
Title of host publicationProceedings of the 12th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, PETS-Winter 2009
DOIs
StatePublished - 2009
Externally publishedYes
Event12th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, PETS-Winter 2009 - Snowbird, UT, United States
Duration: Dec 7 2009Dec 9 2009

Publication series

NameProceedings of the 12th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, PETS-Winter 2009

Conference

Conference12th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, PETS-Winter 2009
Country/TerritoryUnited States
CitySnowbird, UT
Period12/7/0912/9/09

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