Multi-Domain Network Performance Analysis for Vehicle Tracking in Aerial Video

Oliver Sumari H. Felix, C. Juan
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Abstract

The Drone is an unmanned aerial vehicle widely used to take pictures and record videos at high altitude, recording information for applications such as video surveillance, to be able to detect cars and people in real time, the main problem is that both the drone as objects are move, this make difficult the track objects with traditional techniques. Faced this problem, the present research proposes the use of convolutional neural network with multidomain learning (MDNet) and camera movement models for the detection and monitoring of cars based on aerial videos. The propouse obtaining very good results in compare with traditional methods, obtaining a 90 % of success in object tracking, which is useful for practical applications.
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航拍视频中飞行器跟踪的多域网络性能分析
无人机是一种广泛用于高空拍照和录制视频的无人机,为视频监控等应用记录信息,能够实时检测汽车和人,主要问题是无人机作为物体都在移动,这使得用传统技术难以跟踪物体。针对这一问题,本研究提出将卷积神经网络与多域学习(MDNet)结合摄像机运动模型用于基于航拍视频的汽车检测与监控。与传统方法相比,该方法取得了很好的效果,目标跟踪成功率达90%,具有一定的实际应用价值。
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