We describe an end-to-end generative approach for the segmentation and recognition of
human activities. In this approach, a visual representation based on reduced Fisher Vectors …

In order to reduce the computational complexity, most of the video classification approaches
represent video data at frame level. In this paper we investigate a novel perspective that …

In this paper, we propose a new local spatio-temporal descriptor for videos and we propose
a new approach for action recognition in videos based on the introduced descriptor. The …

Human activity recognition is used for many practical applications such as context modeling
in smart cities, surveillance and assisted living. In this paper, we apply a hybrid generative …

This paper proposes a novel framework for Relevance Feedback based on the Fisher
Kernel (FK). Specifically, we train a Gaussian Mixture Model (GMM) on the top retrieval …

The bag-of-words (BoW) representation has successfully been used for human action
recognition from videos. However, one limitation of the standard BoW is that it ignores …

Motivated by applications in areas such as patient monitoring, tele-rehabilitation and
ambient assisted living, analyzing activities of daily living is an active research topic in …

We propose in this work an approach for the automatic extraction and recognition of the
Italian sign language using the RGB, depth and skeletal-joint modalities offered by …

In this paper, we propose a low-cost, non-invasive home activity recognition method using
low-resolution power consumption data. Notably, we tackle the following two challenges …

As research on action recognition matures, the focus is gradually shifting away from
categorizing manuallysegmented clips into basic action units to parsing and understanding …