ATG-PVD: Ticketing Parking Violations on A Drone
| Autor: | Wang, Hengli, Liu, Yuxuan, Huang, Huaiyang, Pan, Yuheng, Yu, Wenbin, Jiang, Jialin, Lyu, Dianbin, Bocus, Mohammud J., Liu, Ming, Pitas, Ioannis, Fan, Rui |
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| Rok vydání: | 2020 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1007/978-3-030-66823-5_32 |
| Popis: | 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. Comment: 17 pages, 11 figures and 3 tables. This paper is accepted by ECCV Workshops 2020 |
| Databáze: | arXiv |
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