Abstract
Foreground blobs in a mixed stereo pair of videos (visible and infrared sensors) allow a coarse evaluation of the distances between each blob and the uncalibrated cameras. The main goals of this work are to find common feature points in each type of image and to create pairs of corresponding points in order to obtain coarse positionning of blobs in space. Feature points are found by two methods: the skeleton and the Discrete Curve Evolution (DCE) algorithm. For each method, a feature-based algorithm creates the pairs of points. Blob pairing can help to create those pairs of points. Finally, a RANSAC algorithm filters all pairs of points in order to respect the epipolar geometrical constraints. The median horizontal disparities for each pair of blobs are evaluated with two different ground truths. In most cases, the nearest blob is detected and disparities are as accurate as the background subtraction allows.
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St-Onge, PL., Bilodeau, GA. (2007). Visible and Infrared Sensors Fusion by Matching Feature Points of Foreground Blobs. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2007. Lecture Notes in Computer Science, vol 4842. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76856-2_1
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DOI: https://doi.org/10.1007/978-3-540-76856-2_1
Publisher Name: Springer, Berlin, Heidelberg
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