ActiveNet: A computer-vision based approach to determine lethargy
This addresses lethargy monitoring for individuals in settings like online classes or surveillance, but it appears incremental as it builds on existing pose detection and classical machine learning methods.
The paper tackles the problem of detecting physical activeness levels in real-time using a single monocular image of a person, proposing a multi-stage computer vision approach that achieved unspecified results.
The outbreak of COVID-19 has forced everyone to stay indoors, fabricating a significant drop in physical activeness. Our work is constructed upon the idea to formulate a backbone mechanism, to detect levels of activeness in real-time, using a single monocular image of a target person. The scope can be generalized under many applications, be it in an interview, online classes, security surveillance, et cetera. We propose a Computer Vision based multi-stage approach, wherein the pose of a person is first detected, encoded with a novel approach, and then assessed by a classical machine learning algorithm to determine the level of activeness. An alerting system is wrapped around the approach to provide a solution to inhibit lethargy by sending notification alerts to individuals involved.