Activity recognition is one of the most prolific fields of research. For this reason, there are new fields of research that expand the possibilities of the activity recognition: Group behavior recognition. This field does not limit the number of elements in the scene, and there are a lot of new elements that must be analyzed. Each group, like each individual element, has its behavior, but this ...

This paper proposes a metric learning based approach for human activity recognition with two main objectives: (1) reject unfamiliar activities and (2) learn with few examples. We show that our approach outperforms all state-of-the-art methods on numerous standard datasets for traditional action classification problem. Furthermore, we demonstrate that our method not only can accurately label act...

In this paper we examine several factors that influence the evaluation of multi-class, continuous activity recognition. Currently, there is no standard metric for evaluating and comparing such systems, although many possible error formulations and performance metrics could be adapted from other domains. In order to make progress toward a standard metric appropriate for evaluating activity recog...

Abstract. We present a method that allows to improve activity recognition using temporal and spatial context. We investigate how incremental learning of long-term human activity patterns improves the accuracy of activity classification over time. Two datasets collected over several months containing hand-annotated activity in residential and office environments were chosen to evaluate the appro...

OF THE DISSERTATION A Physics-Based, Neurobiologically-Inspired Stochastic Framework for Activity Recognition