Fast human detection for intelligent monitoring using surveillance visible sensors

Human detection using visible surveillance sensors is an important and challenging work for intruder detection and safety management. The biggest barrier of real-time human detection is the computational time required for dense image scaling and scanning windows extracted from an entire image. This...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2014-11, Vol.14 (11), p.21247-21257
Main Authors: Ko, Byoung Chul, Jeong, Mira, Nam, JaeYeal
Format: Article
Language:English
Subjects:
adaptive ROI
Algorithms
Cameras
CaRF
Computation
divide-and-conquer
Hough windows map
Human
human detection
Image detection
Methods
Optimization
Sensors
Surveillance
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Summary:Human detection using visible surveillance sensors is an important and challenging work for intruder detection and safety management. The biggest barrier of real-time human detection is the computational time required for dense image scaling and scanning windows extracted from an entire image. This paper proposes fast human detection by selecting optimal levels of image scale using each level's adaptive region-of-interest (ROI). To estimate the image-scaling level, we generate a Hough windows map (HWM) and select a few optimal image scales based on the strength of the HWM and the divide-and-conquer algorithm. Furthermore, adaptive ROIs are arranged per image scale to provide a different search area. We employ a cascade random forests classifier to separate candidate windows into human and nonhuman classes. The proposed algorithm has been successfully applied to real-world surveillance video sequences, and its detection accuracy and computational speed show a better performance than those of other related methods.
ISSN:1424-8220
1424-8220
DOI:10.3390/s141121247