Computer Vision-Based Human Activity Recognition
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".
Deadline for manuscript submissions: 15 December 2024 | Viewed by 1423
Special Issue Editor
Interests: Internet of Things; ubiquitous computing; smart environments; spatial-awareness; pervasive games; security; privacy
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Humans perform a common set of (physical) activities of daily living (ADLs) necessary for self-care and living independently, involving body-part versus whole-body movement. Yet, humans also perform a richer variety of ADLs applied to entertainment, health, sports, surveillance, transport, leisure, and work, involving single and groups of humans scaling up to large crowds. Increasing (and autonomous) automation, changes to the physical environment, and the ageing yet increasing population will affect our ADLs. Hence, recognising, modelling, and analysing ADLs are essential and have many benefits and applications. Whilst a range of sensors can be used for human activity recognition (HAR), the focus here is on the use of computer vision (CV) used for HAR that includes a range of cameras—micro to macro, short-range to remote, stationary versus mobile, and visible versus non-visible light—and can involve the use of hybrid (non-visual plus) visual sensor fusion. This is being driven by advances in micro-sensors, cheaper high-resolution cameras, the increased embedding of cameras into the environment, and improvements in computer vision object recognition and artificial intelligence. Note that most HAR goes beyond pure human recognition and involves relevant physical object recognition to greatly aid this too. This SI targets innovations that support the narrative given above. It also includes new methods and designs for systems based on the Internet of Things; cyber-physical and embedded systems; sensor data acquisition; sensor data fusion; data analytics involving probabilistic and digital twin models to classify, predict, and simulate HAR; data science and AI; and data visualisation and decision support for HAR. Note that accepted papers need to have a viable computer vision-sensing element for HAR.
Dr. Stefan Poslad
Guest Editor
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Keywords
- human activity recognition (HAR)
- activities of daily living (ADLs)
- computer vision (CV)
- sensor data fusion for CV
- AI and data science for CV
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