An iterative integrated framework for thermal-visible image registration, sensor fusion, and people tracking for video surveillance applications

Authors:
Atousa Torabi;Guillaume Massé;Guillaume-Alexandre Bilodeau
Affiliations:
LITIV Laboratory, Department of Computer and Software Engineering, ícole Polytechnique de Montréal, P.O. Box 6079, Station Centre-ville, Montréal, Québec, Canada H3C 3A7;LITIV Laboratory, Department of Computer and Software Engineering, ícole Polytechnique de Montréal, P.O. Box 6079, Station Centre-ville, Montréal, Québec, Canada H3C 3A7;LITIV Laboratory, Department of Computer and Software Engineering, ícole Polytechnique de Montréal, P.O. Box 6079, Station Centre-ville, Montréal, Québec, Canada H3C 3A7
Venue:
Computer Vision and Image Understanding
Year:
2012

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Abstract

In this work, we propose a new integrated framework that addresses the problems of thermal-visible video registration, sensor fusion, and people tracking for far-range videos. The video registration is based on a RANSAC trajectory-to-trajectory matching, which estimates an affine transformation matrix that maximizes the overlapping of thermal and visible foreground pixels. Sensor fusion uses the aligned images to compute sum-rule silhouettes, and then constructs thermal-visible object models. Finally, multiple object tracking uses blobs constructed in sensor fusion to output the trajectories. Results demonstrate the advantage of our proposed framework in obtaining better results for both image registration and tracking than separate image registration and tracking methods.