Abstract Human Action Recognition (HAR) has achieved a remarkable milestone in the field
of computer vision. Apart from its varied applications in human–computer interactions …

In this paper, we propose a structured feature learning framework to reason the correlation
among body joints at the feature level in human pose estimation. Different from existing …

Recently, most of the state-of-the-art human pose estimation methods are based on
heatmap regression. The final coordinates of keypoints are obtained by decoding heatmap …

Tomatoes are widely grown all over the world and are one of the favourite vegetables of
humanity. Tomato harvesting requires a lot of labor. With the aging population and the …

Numerous human actions such as “Phoning,”“PlayingGuitar,” and “RidingHorse” can be
inferred by static cue‐based approaches even if their motions in video are available …

Existing methods to recognize actions in static images take the images at their face value,
learning the appearances---objects, scenes, and body poses---that distinguish each action …

Complex activities often involve multiple humans utilizing different objects to complete
actions (eg, in healthcare settings, physicians, nurses, and patients interact with each other …

Human pose estimation plays an important role in many computer vision tasks and has been
studied for many decades. However, due to complex appearance variations from poses …

Both spatial and temporal patterns provide crucial information for recognizing human
actions. However, lack of temporal information in still images is a major obstacle in single …

Most approaches to human attribute and action recognition in still images are based on
image representation in which multi-scale local features are pooled across scale into a …