Objects height estimation implementing an uncalibrated camera

Height estimation of objects can be implemented both for soft-biometrics and as an object tracking feature. In first case we can eliminate some possible subjects having considerably different height than the observed one, and focus on determining more distinctive remote identification features, like colour, face or ear, and search for similar subjects in a smaller set of possible candidates. For object tracking it can be used for temporal and spatial correspondence analysis as well or simultaneously for both in case of having different cameras. In this thesis we propose a novel method for automatic estimation of height using an uncalibrated camera. Nowadays such cameras can be found in any corner for different purposes like as for security reasons. A crucial moment in height estimation is finding vanishing points. In the method we use RANSAC to estimate best vanishing point from several estimated candidate points. The method has the new advantages that from different frames and their respective height estimations, automatically determines certain reasonable heights depending on height measurements distribution. With spreading of camera implementation in common applications, we believe this new software can be widely applied in as many fields as it can be imagined