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Local Region Proposing for Frame-Based Vehicle Detection in Satellite Videos

School of Engineering and Information Technology, University of New South Wales, Canberra 2612, Australia
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Remote Sens. 2019, 11(20), 2372; https://doi.org/10.3390/rs11202372
Received: 31 August 2019 / Revised: 2 October 2019 / Accepted: 8 October 2019 / Published: 12 October 2019
(This article belongs to the Special Issue Remote Sensing for Target Object Detection and Identification)
Current new developments in remote sensing imagery enable satellites to capture videos from space. These satellite videos record the motion of vehicles over a vast territory, offering significant advantages in traffic monitoring systems over ground-based systems. However, detecting vehicles in satellite videos are challenged by the low spatial resolution and the low contrast in each video frame. The vehicles in these videos are small, and most of them are blurred into their background regions. While region proposals are often generated for efficient target detection, they have limited performance on satellite videos. To meet this challenge, we propose a Local Region Proposing approach (LRP) with three steps in this study. A video frame is segmented into semantic regions first and possible targets are then detected in these coarse scale regions. A discrete Histogram Mixture Model (HistMM) is proposed in the third step to narrow down the region proposals by quantifying their likelihoods towards the target category, where the training is conducted on positive samples only. Experiment results demonstrate that LRP generates region proposals with improved target recall rates. When a slim Fast-RCNN detector is applied, LRP achieves better detection performance over the state-of-the-art approaches tested.
Keywords: satellite videos; region proposals; convolutional neural networks; tiny and dim target detection; component mixture model satellite videos; region proposals; convolutional neural networks; tiny and dim target detection; component mixture model
MDPI and ACS Style

Zhang, J.; Jia, X.; Hu, J. Local Region Proposing for Frame-Based Vehicle Detection in Satellite Videos. Remote Sens. 2019, 11, 2372.

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