Several computer vision tasks exploit a succinct representation of the visual content in the form of sets of local features. Given an input image, feature extraction algorithms identify key-points and assign to each of them a descriptor, based on the characteristics of the surrounding visual content. Several tasks might require local features to be extracted from a video sequence, on a frame-by-frame basis. Although temporal downsampling has been proven to be an effective solution for mobile augmented reality and visual search, high temporal resolution is a key requirement for time-critical applications such as object tracking, event recognition, pedestrian detection, surveil- lance. In recent years, more and more computationally efficient visual feature detectors and descriptors have been proposed. Nonetheless, such approaches are tailored to still images. In this paper we propose a fast key-point detection algorithm for video sequences, that exploits the temporal coherence of the sequence of key-points. Ac- cording to the proposed method, each frame is preprocessed so as to identify the parts of the input frame for which key-point detection and description need to be performed. Our experiments show that it is possible to achieve a reduction in computational time of up to 40%, without significantly affecting the task accuracy.