This book offers a systematic, comprehensive, and timely review on
V-HAR, and it covers the related tasks, cutting-edge technologies,
and applications of V-HAR, especially the deep learning-based
approaches. The field of Human Activity Recognition (HAR) has
become one of the trendiest research topics due to the availability
of various sensors, live streaming of data and the advancement in
computer vision, machine learning, etc. HAR can be extensively used
in many scenarios, for example, medical diagnosis, video
surveillance, public governance, also in human-machine interaction
applications. In HAR, various human activities such as walking,
running, sitting, sleeping, standing, showering, cooking, driving,
abnormal activities, etc., are recognized. The data can be
collected from wearable sensors or accelerometer or through video
frames or images; among all the sensors, vision-based sensors are
now the most widely used sensors due to their low-cost,
high-quality, and unintrusive characteristics. Therefore,
vision-based human activity recognition (V-HAR) is the most
important and commonly used category among all HAR technologies.
The addressed topics include hand gestures, head pose, body
activity, eye gaze, attention modeling, etc. The latest
advancements and the commonly used benchmark are given.
Furthermore, this book also discusses the future directions and
recommendations for the new researchers.
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