Abstract:
A simple activity recognition model can allow a single human person to monitor all our
surrounding with the purpose to ensure safety and privacy while preserving maintenance
cost and efficiency with the soaring level of precision. This monitoring system with realtime video surveillance could be deployed for patients and the elderly in a hospital or old
age home along with various human activity on important area such as the airport. For
speedy analyze of action and accurate result while working with complex human
behaviour, we decided to use YOLOv4 (You Only Look Once) algorithm which is the
latest and the fastest among them all. This technique uses bounding boxes to highlight the
action. In this case, we have collected 4,674 number of different data from the hospital or
different condition ourselves for fastest accuracy with the one of the largest data-set ever
used in such kind of project. During our research, we had divided our action into three
different class which are standing, sitting and walking. This model was able to detect and
recognize multiple patients or other regular person activity and multiple human activities
tracing support at once. After completing our project, this model manages an average
accuracy of 94.6667% while recognizing image and about 63.00% while recognizing
activity from video file. We also work on two other different projects with TensorFlow and
OpenPose while the YOLOv4 perform better than those two. In future, more complex data
can be added and prediction can be implemented which will improve and add utility to this
project.
