Abstract

Over the years, several spatio-temporal interest point detectors have been proposed. While some detectors can only extract a sparse set of scale- invariant features, others allow for the detection of a larger amount of features at user-defined scales. This paper presents for the first time spatio-temporal interest points that are at the same time scale-invariant (both spatially and temporally) and densely cover the video content. Moreover, as opposed to earlier work, the fea- tures can be computed efficiently. Applying scale-space theory, we show that this can be achieved by using the determinant of the Hessian as the saliency measure. Computations are speeded-up further through the use of approximative box- filter operations on an integral video structure. A quantitative evaluation and experi- mental results on action recognition show the strengths of the proposed detector in terms of repeatability, accuracy and speed, in comparison with previously pro- posed detectors.