Abstract
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.
Highlights
With the popularization of cars, the number of vehicles, as well as the traffic pressure, have rapidly increased
We introduce a motion compensation method based on homography to adjust the position of the vehicle, so as to eliminate the background motion caused by camera movement and ensure the consistency of the target’s spatial coordinates in successive frames
We proposed an adaptive framework for multi-vehicle ground speed estimation in airborne videos
Summary
With the popularization of cars, the number of vehicles, as well as the traffic pressure, have rapidly increased. Vehicle ground speed estimation is one fundamental and important part of the intelligent transportation system, which is critical to improving the efficiency and safety of roadway transportation [2]. It has many applications in many fields, such as the autonomous landing of UAVs [3], social anti-terrorism, and public security protection. Video-based technology does not require complex and expensive hardware, is easy to install and maintain, and has a large monitoring range. Owing to the development and maturity of video processing technology, this approach has grown rapidly
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