Vehicle Speed Detection by Multi-source Images from UAV

doi:10.11947/j.AGCS.2018.20170506

Abstract

Abstract: Traffic plays a vital role in people's life and social economy.Vehicle speed detection is an important part of intelligent transportation system.This paper focus on the vehicle speed detection based on multi-source data from autonomous unmanned aerial vehicle (UAV).Firstly,we build a multi-source data acquisition system on UAV for visible image and thermal infrared image.Secondly,we utilize "You only look once" (YOLO),which is a deep learning framework for vehicle detection.Finally,we track the vehicle based on Kalman filter and calculated the vehicle speed according to the result of vehicle tracking.This paper adopts the UAV platform to increase the flexibility.While the use of multi-source data improves the accuracy of the vehicle detection and tracks the vehicle in different illumination.The result of experiments shows that the strategy is effective and robust,which provides an efficient and flexible monitoring mode for traffic management department.

Key words: unmanned aerial vehicle, vehicle speed detection, deep learning, thermal infrared image

CLC Number:

P227

JIANG Shangjie, LUO Bin, HE Peng, YANG Guopeng, GU Yaping, LIU Jun, ZHANG Yun, ZHANG Liangpei. Vehicle Speed Detection by Multi-source Images from UAV[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(9): 1228-1237.

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