AirCamRTM: Enhancing Vehicle Detection for Efficient Aerial Camera-based Road Traffic Monitoring

Efficient road traffic monitoring is playing a fundamental role in successfully resolving traffic congestion in cities. Unmanned Aerial Vehicles (UAVs) or drones equipped with cameras are an attractive proposition to provide flexible and infrastructure-free traffic monitoring. However, real-time traffic monitoring from UAV imagery poses several challenges, due to the large image sizes and presence of non-relevant targets. In this paper, we propose the AirCam-RTM framework that combines road segmentation and vehicle detection to focus only on relevant vehicles, which as a result, improves the monitoring performance by ~2 × and provides ~ 18% accuracy improvement. Furthermore, through a real experimental setup we qualitatively evaluate the performance of the proposed approach, and also demonstrate how it can be used for real-time traffic monitoring using UAVs. Dataset: https://zenodo.org/record/7053442"© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works." R. Makrigiorgis, N. Hadjittoouli, C. Kyrkou and T. Theocharides, "AirCamRTM: Enhancing Vehicle Detection for Efficient Aerial Camera-based Road Traffic Monitoring," 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, 2022, pp. 3431-3440, doi: 10.1109/WACV51458.2022.00349