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In the context of a localization and tracking application, we developed astereo vision system based on cheap low-resolution 80x60 pixels thermalcameras. We proposed a threefold sub-pixel stereo matching framework (called STfor Subpixel Thermal): 1) robust features extraction method based on phasecongruency, 2) rough matching of these features in pixel precision, and 3)refined matching in sub-pixel accuracy based on local phase coherence. Weperformed experiments on our very low-resolution thermal images (acquired usinga stereo system we manufactured) as for high-resolution images from a benchmarkdataset. Even if phase congruency computation time is high, it was able toextract two times more features than state-of-the-art methods such as ORB orSURF. We proposed a modified version of the phase correlation applied in thephase congruency feature space for sub-pixel matching. Using simulated stereo,we investigated how the phase congruency threshold and the sub-image size ofsub-pixel matching can influence the accuracy. We then proved that given ourstereo setup and the resolution of our images, being wrong of 1 pixel leads toa 500 mm error in the Z position of the point. Finally, we showed that ourmethod could extract four times more matches than a baseline method ORB +OpenCV KNN matching on low-resolution images. Moreover, our matches were morerobust. More precisely, when projecting points of a standing person, ST got astandard deviation of 300 mm when ORB + OpenCV KNN gave more than 1000 mm.

Sub-pixel matching method for low-resolution thermal stereo images