Recognizing human activities from video sequences or still images is a challenging task due
to problems, such as background clutter, partial occlusion, changes in scale, viewpoint …

Human activity recognition (HAR) systems attempt to automatically identify and analyze
human activities using acquired information from various types of sensors. Although several …

Movement dataset reviews exist but are limited in coverage, both in terms of size and
research discipline. While topic-specific reviews clearly have their merit, it is critical to have a …

Human activity recognition (HAR) is a complex and multifaceted problem. The research
community has reported numerous approaches to perform HAR. Along with HAR …

In this paper, a human behavior recognition method using multimodal features is presented.
We focus on modeling individual and social behaviors of a subject (eg, friendly/aggressive …

In many human activity recognition systems the size of the unlabeled training data may be
significantly large due to expensive human effort required for data annotation. Moreover, the …

In this work, a supervised probabilistic approach is proposed that integrates the learning
using privileged information (LUPI) paradigm into a hidden conditional random field (HCRF) …

Hidden conditional random fields (HCRFs) are a powerful supervised classification system,
which is able to capture the intrinsic motion patterns of a human action. However, finding the …

Abstract Human Activity Recognition (HAR) is a vast and exciting topic for researchers and
students. HAR aims to recognize activities by observing the actions of subjects and …

Skeleton-based human-action-recognition (SBHAR) has wide applications in cognitive
science and automatic surveillance. However, the most challenging and crucial task of the …