Understanding human actions in visual data is tied to advances in complementary research
areas including object recognition, human dynamics, domain adaptation and semantic …

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 …

Within a large range of applications in computer vision, Human Action Recognition has
become one of the most attractive research fields. Ambiguities in recognizing actions does …

This work presents a new approach to learning a frame-based classifier on weakly labelled
sequence data by embedding a CNN within an iterative EM algorithm. This allows the CNN …

In this work we present a new approach to the field of weakly supervised learning in the
video domain. Our method is relevant to sequence learning problems which can be split up …

We present an unsupervised representation learning approach that compactly encodes the
motion dependencies in videos. Given a pair of images from a video clip, our framework …

Human action Recognition for unknown views, is a challenging task. We propose a deep
view-invariant human action recognition framework, which is a novel integration of two …

Action recognition and pose estimation from video are closely related tasks for
understanding human motion, most methods, however, learn separate models and combine …

Video-based human action recognition has become one of the most popular research areas
in the field of computer vision and pattern recognition in recent years. It has a wide variety of …

In this competitive world, the supervision and monitoring of human resources are primary
and necessary tasks to drive context-aware applications. Advancement in sensor and …