Human action recognition in videos: a comparative evaluation of the classical and velocity adaptation space-time interest points techniques

Abstract

Human action recognition is a topic widely studied over time, using numerous techniques and methods to solve a fundamental problem in automatic video analysis. Basically, a traditional human action recognition system collects video frames of human activities, extracts the desired features of each human skeleton and classify them to distinguish human gesture. However, almost all of these approaches roll out the space-time information of the recognition process. In this paper we present a novel use of an existing state-of-the-art space-time technique, the Space-Time Interest Point (STIP) detector and its velocity adaptation, to human action recognition process. Using STIPs as descriptors and a Support Vector Machine classifier, we evaluate four different public video datasets to validate our methodology and demonstrate its accuracy in real scenarios.