Recent work shows how to use local spatio-temporal features to learn models of realistic human actions from video. However, existing methods typically rely on a predefined spatial...
We propose a new method for human action recognition from video sequences using latent topic models. Video sequences are represented by a novel “bag-of-words” representation, w...
In this paper, we present a novel approach for automatically learning a compact and yet discriminative appearance-based human action model. A video sequence is represented by a ba...
We present a computational framework capable of labeling the effort of an action corresponding to the perceived level of exertion by the performer (low ? high). The approach initi...
This paper addresses the problem of classifying human actions in a video sequence. A representation eigenspace approach based on the PCA algorithm is used to train the classifier...
Carlo Colombo, Dario Comanducci, Alberto Del Bimbo