ATG-PVD: Ticketing Parking Violations on A Drone
This addresses the problem of efficiently monitoring parking violations for urban management, but it is incremental as it builds on existing methods like CNNs and SLAM.
The paper tackles automated parking violation detection using a drone by introducing a suspect-and-investigate framework with components for optical flow, car detection, and verification, achieving state-of-the-art speed and accuracy in optical flow and better performance than Faster-RCNN for detection.
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.