An Efficient Dense and Scale-Invariant Spatio-Temporal Interest Point Detector

Over the years, several spatio-temporal interest point detectors have been proposed. While some detectors can only extract a sparse set of scaleinvariant 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 features 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 experimental results on action recognition show the strengths of the proposed detector in terms of repeatability, accuracy and speed, in comparison with previously proposed detectors.

• Type: Conference
• Year: 2008
• Where: ECCV
• Authors: Geert Willems, Tinne Tuytelaars, Luc J. Van Gool
• Keywords: Approximative Box-filter Operations, Computer Vision, ECCV 2008, Integral Video Structure, Point Detectors, Scale-space Theory, User-defined Scales