Fast spatiotemporal MACH filter for action recognition

Abstract

Human action recognition has been an active field of research in computer vision community for the last decade. The spatiotemporal MACH (maximum average correlation height) filter approach has proved to be a very efficient method to solve the problem. It captures the intra-class variability and produces a very high response at the spatiotemporal location $$(x,y,t)$$ where the action is present in a video. Its computation cost is significantly lower than any other action recognition approach. However, faster algorithm is always needed to perform a computer vision task in real-time. Therefore, we propose a very efficient algorithm for normalized spatiotemporal MACH filtering for action recognition. It is based on the computations performed both in the frequency domain as well as the spatiotemporal domain exploiting integral video. We compare its speed with that of the relevant traditional algorithms and show that our approach drastically outperforms all of them.