ATG-PVD: Ticketing Parking Violations on A Drone
Abstract
In this paper, we introduce a novel suspect-and-investigate framework, which can be easily embedded in a drone for automated parking violation detection (PVD). Our proposed framework consists of: 1) SwiftFlow, an efficient and accurate convolutional neural network (CNN) for unsupervised optical flow estimation; 2) Flow-RCNN, a flow-guided CNN for car detection and classification; and 3) an illegally parked car (IPC) candidate investigation module developed based on visual SLAM. The proposed framework was successfully embedded in a drone from ATG Robotics. The experimental results demonstrate that, firstly, our proposed SwiftFlow outperforms all other state-of-the-art unsupervised optical flow estimation approaches in terms of both speed and accuracy; secondly, IPC candidates can be effectively and efficiently detected by our proposed Flow-RCNN, with a better performance than our baseline network, Faster-RCNN; finally, the actual IPCs can be successfully verified by our investigation module after drone re-localization.
Cite
@article{arxiv.2008.09305,
title = {ATG-PVD: Ticketing Parking Violations on A Drone},
author = {Hengli Wang and Yuxuan Liu and Huaiyang Huang and Yuheng Pan and Wenbin Yu and Jialin Jiang and Dianbin Lyu and Mohammud J. Bocus and Ming Liu and Ioannis Pitas and Rui Fan},
journal= {arXiv preprint arXiv:2008.09305},
year = {2021}
}
Comments
17 pages, 11 figures and 3 tables. This paper is accepted by ECCV Workshops 2020