This paper presents a new method to detect video repetitions in a TV stream. This method aims at reducing the number of image descriptors that have to be computed. First, shot durations are used as hash keys to propose a first set of potential repetitions. Second, a statistical test and visual descriptors are used to verify and complete these first guesses in order to improve the results by reducing the number of false detections and by retrieving the maximal length of the repeated segments. The method is tested in the context of a TV structuring application using one month of continuous TV stream.