Trashbusters: Deep Learning Approach for Litter Detection and Tracking
Kashish Jain Manthan Juthani Jash Jain Anant V. Nimkar
The illegal disposal of trash is a major public health and environmental
concern. Disposing of trash in unplanned places poses serious health and
environmental risks. We should try to restrict public trash cans as much as
possible. This research focuses on automating the penalization of litterbugs,
addressing the persistent problem of littering in public places. Traditional
approaches relying on manual intervention and witness reporting suffer from
delays, inaccuracies, and anonymity issues. To overcome these challenges, this
paper proposes a fully automated system that utilizes surveillance cameras and
advanced computer vision algorithms for litter detection, object tracking, and
face recognition. The system accurately identifies and tracks individuals
engaged in littering activities, attaches their identities through face
recognition, and enables efficient enforcement of anti-littering policies. By
reducing reliance on manual intervention, minimizing human error, and providing
prompt identification, the proposed system offers significant advantages in
addressing littering incidents. The primary contribution of this research lies
in the implementation of the proposed system, leveraging advanced technologies
to enhance surveillance operations and automate the penalization of litterbugs.