Smart City Community Watch—Camera-Based Community Watch for Traffic and Illegal Dumping

Authors

Nupur Pathak, San Jose State University
Gangotri Biswal, San Jose State University
Megha Goushal, San Jose State University
Vraj Mistry, San Jose State University
Palak Shah, San Jose State University
Fenglian Li, Taiyuan University of Technology
Jerry Gao, San Jose State UniversityFollow

Publication Date

8-1-2024

Document Type

Article

Publication Title

Smart Cities

Volume

7

Issue

4

DOI

10.3390/smartcities7040088

First Page

2232

Last Page

2257

Abstract

Highlights: What are the main findings? The smart city community watch program developed using YOLOv5 for object detection and DeepSORT for multi-object tracking achieves 97% accuracy in detecting illegal dumping. The web-based application integrates person detection, trash detection, license plate detection and extraction, and a decision algorithm aiding government agencies to monitor and effectively manage illegal dumping. What are the implications of the main findings? With the 97% detection accuracy and real-time detection capabilities of the YOLOv5- and DeepSORT-based solution, this solution can help in saving government expenditure to clean up illegal dumping. The solution can be integrated with smart city programs such as smart waste management initiatives, aid in effective and proactive public management, and promote public health. The United States is the second-largest waste generator in the world, generating 4.9 pounds (2.2 kg) of Municipal Solid Waste (MSW) per person each day. The excessive amount of waste generated poses serious health and environmental risks, especially because of the prevalence of illegal dumping practices, including improper waste disposal in unauthorized areas. To clean up illegal dumping, the government spends approximately USD 600 per ton, which amounts to USD 178 billion per year. Municipalities face a critical challenge to detect and prevent illegal dumping activities. Current techniques to detect illegal dumping have limited accuracy in detection and do not support an integrated solution of detecting dumping, identifying the vehicle, and a decision algorithm notifying the municipalities in real-time. To tackle this issue, an innovative solution has been developed, utilizing a You Only Look Once (YOLO) detector YOLOv5 for detecting humans, vehicles, license plates, and trash. The solution incorporates DeepSORT for effective identification of illegal dumping by analyzing the distance between a human and the trash’s bounding box. It achieved an accuracy of 97% in dumping detection after training on real-time examples and the COCO dataset covering both daytime and nighttime scenarios. This combination of YOLOv5, DeepSORT, and the decision module demonstrates robust capabilities in detecting dumping. The objective of this web-based application is to minimize the adverse effects on the environment and public health. By leveraging advanced object detection and tracking techniques, along with a user-friendly web application, it aims to promote a cleaner, healthier environment for everyone by reducing improper waste disposal.

Funding Number

202304041101035

Keywords

big data, computer vision, deep learning, illegal dumping, scene classification, smart city, video surveillance

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.

Department

Applied Data Science

Recommended Citation

Nupur Pathak, Gangotri Biswal, Megha Goushal, Vraj Mistry, Palak Shah, Fenglian Li, and Jerry Gao. "Smart City Community Watch—Camera-Based Community Watch for Traffic and Illegal Dumping" Smart Cities (2024): 2232-2257. https://doi.org/10.3390/smartcities7040088