  A number of 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 a set of keypoints and assign to each of
them a description vector, based on the characteristics of the visual content
surrounding the interest point. 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, surveillance. In recent years, more and more
computationally efficient visual feature detectors and decriptors have been
proposed. Nonetheless, such approaches are tailored to still images. In this
paper we propose a fast keypoint detection algorithm for video sequences, that
exploits the temporal coherence of the sequence of keypoints. According to the
proposed method, each frame is preprocessed so as to identify the parts of the
input frame for which keypoint 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.
