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.
