Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
The visual analysis of human movement: a survey
Computer Vision and Image Understanding
Multiple view geometry in computer vision
Multiple view geometry in computer vision
Handbook of Image and Video Processing
Handbook of Image and Video Processing
A Two-Stage Template Approach to Person Detection in Thermal Imagery
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Background-subtraction using contour-based fusion of thermal and visible imagery
Computer Vision and Image Understanding
A Real Time Human Detection System Based on Far Infrared Vision
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
Human detection using oriented histograms of flow and appearance
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Pedestrian detection and tracking with night vision
IEEE Transactions on Intelligent Transportation Systems
Multi-cue-based crowd segmentation in stereo vision
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
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In this paper, we propose a human detection process using Far-Infrared (FIR) and daylight cameras mounted on a stereovision setup. Although daylight or FIR cameras have long been used to detect pedestrians, they nonetheless suffer from known limitations. In this paper, we present how both can collaborate inside a stereovision setup to reduce the false positive rate inherent to their individual use. Our detection method is based on two distinctive steps. First, human positions are detected in both FIR and daylight images using a cascade of boosted classifiers. Then, both results are fused based on the geometric information of the sterovision system. In this paper, we present how human positions are localized in images, and how the decisions taken by each camera are fused together. In order to gauge performances, a quantitative evaluation based on an annotated dataset is presented.