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.