Trashbusters: Deep Learning Approach for Litter Detection and Tracking

TL;DR

This work tackles automated enforcement of anti-littering by linking litter detection, robust tracking, and long-distance face recognition. The proposed Trashbusters pipeline combines a fine-tuned YOLOv4 litter detector, an UKF-enhanced DeepSORT tracker, and FRAD with Multi-task CNN and ArcFace to identify offenders via face matching against a database. Key contributions include integrating litter detection with non-linear–motion tracking and distance-face recognition, plus a dataset- and pipeline-oriented evaluation showing YOLOv4 offers strong detection performance and the improved tracker handles occlusion and drift more effectively. The approach aims to enable prompt, automated penalization of litterbugs, potentially improving enforcement efficiency, accountability, and public space cleanliness, albeit with limitations in object variety and lighting conditions that warrant further real-time deployment studies and collaboration with government agencies.

Abstract

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.

Figures (3)

• Figure 1: DeepSORT
• Figure 3: Architectural Diagram
• Figure 4: For various object identification methods, the trade-off between accuracy (AP & AP50) and computing efficiency (FPS).