  Video Captioning is considered to be one of the most challenging problems in
the field of computer vision. Video Captioning involves the combination of
different deep learning models to perform object detection, action detection,
and localization by processing a sequence of image frames. It is crucial to
consider the sequence of actions in a video in order to generate a meaningful
description of the overall action event. A reliable, accurate, and real-time
video captioning method can be used in many applications. However, this paper
focuses on one application: video captioning for fostering and facilitating
physical activities. In broad terms, the work can be considered to be assistive
technology. Lack of physical activity appears to be increasingly widespread in
many nations due to many factors, the most important being the convenience that
technology has provided in workplaces. The adopted sedentary lifestyle is
becoming a significant public health issue. Therefore, it is essential to
incorporate more physical movements into our daily lives. Tracking one's daily
physical activities would offer a base for comparison with activities performed
in subsequent days. With the above in mind, this paper proposes a video
captioning framework that aims to describe the activities in a video and
estimate a person's daily physical activity level. This framework could
potentially help people trace their daily movements to reduce an inactive
lifestyle's health risks. The work presented in this paper is still in its
infancy. The initial steps of the application are outlined in this paper. Based
on our preliminary research, this project has great merit.
