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Catheters are routinely inserted via vessels to cavities of the heart during fluoroscopic image guided interventions for electrophysiology (EP) procedures such as ablation. During such interventions, the catheter undergoes nonrigid deformation due to physician interaction, patient's breathing, and cardiac motions. EP clinical applications can benefit from fast and accurate automatic catheter tracking in the fluoroscopic images. The typical low quality in fluoroscopic images and the presence of other medical instruments in the scene make the automatic detection and tracking of catheters in clinical environments very challenging. Toward the development of such an application, a robust and efficient method for detecting and tracking the catheter sheath is developed. The proposed approach exploits the clinical setup knowledge to constrain the search space while boosting both tracking speed and accuracy, and is based on a computationally efficient framework to trace the sheath and simultaneously detect one or multiple catheter tips. The algorithm is based on a modification of the fast marching weighted distance computation that efficiently calculates, on the fly, important geodesic properties in relevant regions of the image. This is followed by a cascade classifier for detecting the catheter tips. The proposed technique is validated on 1107 fluoroscopic images acquired on multiple patients across four different clinics, achieving multiple catheter tracking at a rate of 10 images/s with a very low false positive rate of 1.06.