Extracting effective spatial-temporal information is significantly important for video-based
action recognition. Recently 3D convolutional neural networks (3D CNNs) that could …

Cyber–physical systems (CPSs) are evolving from individual systems to collectives of
systems that collaborate to achieve highly complex goals, realizing a cyber–physical system …

Neural architecture search has proven to be highly effective in the design of efficient
convolutional neural networks that are better suited for mobile deployment than hand …

Action recognition is an important and challenging problem in video analysis. Although the
past decade has witnessed progress in action recognition with the development of deep …

In the recent past, human action recognition is inviting increased attention in the automated
video surveillance systems. An efficient human action classification technique in an …

The accurate detection and recognition of human actions play a pivotal role in aerial
surveillance, enabling the identification of potential threats and suspicious behavior. Several …

Abstract 3D convolutional networks, as direct inheritors of 2D convolutional networks for
images, have placed their mark on action recognition in videos. Combined with pretraining …

Any application of machine learning requires feature extraction, whereupon the source data
is processed to yield representations that are germane to obtaining the desired output …

Cyber Physical Systems have been going into a transition phase from individual systems to
a collecttives of systems that collaborate in order to achieve a highly complex cause …

Recognition of human hitting movement in a more specific context of sports like boxing is still
a hard task because the existing systems use manual observation which could be easily …