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Open AccessArticle

An Adaptive Framework for Multi-Vehicle Ground Speed Estimation in Airborne Videos

1
School of Telecommunications Engineering, Xidian University, Xi’an 710000, China
2
School of Computer Science, Northwestern Polytechnical University, Xi’an 710000, China
3
National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, SAIIP, Xi’an 710000, China
*
Authors to whom correspondence should be addressed.
Remote Sens. 2019, 11(10), 1241; https://doi.org/10.3390/rs11101241
Received: 27 March 2019 / Revised: 1 May 2019 / Accepted: 17 May 2019 / Published: 24 May 2019
(This article belongs to the Special Issue Drone Remote Sensing)
With the rapid development of unmanned aerial vehicles (UAVs), UAV-based intelligent airborne surveillance systems represented by real-time ground vehicle speed estimation have attracted wide attention from researchers. However, there are still many challenges in extracting speed information from UAV videos, including the dynamic moving background, small target size, complicated environment, and diverse scenes. In this paper, we propose a novel adaptive framework for multi-vehicle ground speed estimation in airborne videos. Firstly, we build a traffic dataset based on UAV. Then, we use the deep learning detection algorithm to detect the vehicle in the UAV field of view and obtain the trajectory in the image through the tracking-by-detection algorithm. Thereafter, we present a motion compensation method based on homography. This method obtains matching feature points by an optical flow method and eliminates the influence of the detected target to accurately calculate the homography matrix to determine the real motion trajectory in the current frame. Finally, vehicle speed is estimated based on the mapping relationship between the pixel distance and the actual distance. The method regards the actual size of the car as prior information and adaptively recovers the pixel scale by estimating the vehicle size in the image; it then calculates the vehicle speed. In order to evaluate the performance of the proposed system, we carry out a large number of experiments on the AirSim Simulation platform as well as real UAV aerial surveillance experiments. Through quantitative and qualitative analysis of the simulation results and real experiments, we verify that the proposed system has a unique ability to detect, track, and estimate the speed of ground vehicles simultaneously even with a single downward-looking camera. Additionally, the system can obtain effective and accurate speed estimation results, even in various complex scenes. View Full-Text
Keywords: ground vehicle speed estimation; intelligent airborne video surveillance; unmanned aerial vehicle; object detection and tracking; motion compensation ground vehicle speed estimation; intelligent airborne video surveillance; unmanned aerial vehicle; object detection and tracking; motion compensation
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MDPI and ACS Style

Li, J.; Chen, S.; Zhang, F.; Li, E.; Yang, T.; Lu, Z. An Adaptive Framework for Multi-Vehicle Ground Speed Estimation in Airborne Videos. Remote Sens. 2019, 11, 1241.

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