Abstract:
It is conceivable to see how active each individual is at work by observing their
various physical postures and gestures. With conventional systems, it is hard to
constantly monitor every employee and make the best use of them. Our thesis' major
goal is to determine the most effective strategy to utilize each employee's available
work time. The challenge of object detection in visual data has been demonstrated to
be almost fully resolved by deep learning, a method that mimics the information flow
of the human mind. Allowing computers to distinguish not just things but also
activities is one of the upcoming key issues in computer vision. In this work, the
possibilities of deep learning are examined for the particular challenge of activity
recognition in office settings. Data from CCTV footage of several offices during business hours was gathered in order to implement the ‘YOLOv7 object detection’
model. The research implemented a re-labeled dataset of distinct office worker
motions to distinguish between employees' levels of activity. With the assistance of
Flask, we can build a single-page website, deploy our model, and perform
round-the-clock monitoring. After training, the model displays higher
accuracy(95.7%), demonstrating how ideal it is for this situation. By employing this
approach, it is possible to estimate an employee's productivity by observing their
numerous motions while the workplace is in operation. Any official context can use
our concept approach, and deployment services can provide ongoing monitoring.
