ROS-Based Multi-Quadcopter System for Enhancing Crowd Surveillance
Traditional survey methods often struggle with inefficiencies, limited coverage, and a high risk of human fatigue and error, particularly during prolonged or complex surveillance tasks. These constraints underscore the need for an autopilot system capable of real-time crowd monitoring with enhanced accuracy, speed, and reduced human intervention. To address these limitations, this study presents a multi-quadcopter system developed using the Robot Operating System (ROS) aimed at significantly improving the efficacy of crowd surveillance. This system leverages coordinated multi-drone operation, real-time video processing, and precise trajectory control, enabling synchronized monitoring that achieves comprehensive area coverage and reliable crowd detection. Through rigorous experimental testing, the ROS-based multi-quad copter system achieved a crowd detection accuracy of 93.93% while reducing survey duration by 60.12% compared to single-quadcopter configurations. This notable improvement demonstrates the system's capacity to streamline operations and provide timely, accurate surveillance across extensive and complex environments, minimizing the need for human involvement. Ultimately, this multi-quadcopter system delivers a scalable and efficient approach to crowd monitoring, with promising applications in public safety, disaster response, and urban monitoring, where real-time, high-quality data is crucial. The ROS-based platform represents a significant step forward in photographic aerial surveillance, offering a versatile and robust solution for advancing intelligent monitoring capabilities.
Authors
- Pipit Anggraeni (ORCID: https://orcid.org/0000-0003-1986-272X)
- Setyawan Ajie Sukarno (ORCID: https://orcid.org/0000-0002-3136-1832)
- Mikhael Milianka
Institutions
- Bandung Institute of Technology (ID)
Publication Details
- Published
- 2024-12-17
- DOI
- https://doi.org/10.1109/icicyta64807.2024.10913092
- Citations
- 1
- Primary Topic
- Network Security and Intrusion Detection
- Type
- article
- Field-Weighted Citation Impact
- 0.35