| Autor: |
Wang, Hengli, Liu, Yuxuan, Huang, Huaiyang, Pan, Yuheng, Yu, Wenbin, Jiang, Jialin, Lyu, Dianbin, Bocus, Mohammud J., Liu, Ming, Pitas, Ioannis, Fan, Rui |
| Přispěvatelé: |
Bartoli, Adrien, Fusiello, Andrea |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Zdroj: |
Wang, H, Liu, Y, Huang, H, Pan, Y, Yu, W, Jiang, J, Lyu, D, Bocus, M J, Liu, M, Pitas, I & Fan, R 2021, ATG-PVD : Ticketing Parking Violations on a Drone . in A Bartoli & A Fusiello (eds), Computer Vision--ECCV 2020 Workshops . Lecture Notes in Computer Science, vol. 12538, Springer International Publishing AG, pp. 541-557, Computer Vision – ECCV 2020 Workshops, Glasgow, United Kingdom, 23/08/20 . https://doi.org/10.1007/978-3-030-66823-5_32 |
| 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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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