Human activity analysis in videos has increasingly attracted attention in computer vision
research with the massive number of videos now accessible online. Although many …

Due to its various applications, human action recognition has been widely studied and
achieved tremendous progress. However, how to learn an accurate and discriminative …

In this paper, we formulate human action recognition as a novel Multi-Task Sparse Learning
(MTSL) framework which aims to construct a test sample with multiple features from as few …

We propose a sparse-coding framework for activity recognition in ubiquitous and mobile
computing that alleviates two fundamental problems of current supervised learning …

Sparse representation has been applied recently to many signal processing and computer
vision and demonstrated successful results. Inspired by them, we propose an action …

Recently, many sparse coding based approaches have been proposed for human action
recognition. However, most of them focus on learning a discriminative dictionary without …

This paper explores the effectiveness of sparse representations obtained by learning a set of
overcomplete basis (dictionary) in the context of action recognition in videos. Although this …

Human daily activity recognition using mobile personal sensing technology plays a central
role in the field of pervasive healthcare. One major challenge lies in the inherent complexity …

We present a novel descriptor, called spatio-temporal Laplacian pyramid coding (STLPC),
for holistic representation of human actions. In contrast to sparse representations based on …

This paper presents a novel framework for human action recognition based on sparse
coding. We introduce an effective coding scheme to aggregate low-level descriptors into the …