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Detection and Tracking of Moving Pedestrians with a Small Unmanned Aerial Vehicle

School of Computer and Communication Eng., Daegu University, Gyeongsan 38453, Korea
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Appl. Sci. 2019, 9(16), 3359; https://doi.org/10.3390/app9163359
Received: 30 April 2019 / Revised: 29 July 2019 / Accepted: 5 August 2019 / Published: 15 August 2019
(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAVs))
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Abstract

Small unmanned aircraft vehicles (SUAVs) or drones are very useful for visual detection and tracking due to their efficiency in capturing scenes. This paper addresses the detection and tracking of moving pedestrians with an SUAV. The detection step consists of frame subtraction, followed by thresholding, morphological filter, and false alarm reduction, taking into consideration the true size of targets. The center of the detected area is input to the next tracking stage. Interacting multiple model (IMM) filtering estimates the state of vectors and covariance matrices, using multiple modes of Kalman filtering. In the experiments, a dozen people and one car are captured by a stationary drone above the road. The Kalman filter and the IMM filter with two or three modes are compared in the accuracy of the state estimation. The root-mean squared errors (RMSE) of position and velocity are obtained for each target and show the good accuracy in detecting and tracking the target position—the average detection rate is 96.5%. When the two-mode IMM filter is used, the minimum average position and velocity RMSE obtained are around 0.8 m and 0.59 m/s, respectively. View Full-Text
Keywords: UAV/drone imaging; pedestrian detection; multiple target tracking; state estimator UAV/drone imaging; pedestrian detection; multiple target tracking; state estimator
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Yeom, S.; Cho, I.-J. Detection and Tracking of Moving Pedestrians with a Small Unmanned Aerial Vehicle. Appl. Sci. 2019, 9, 3359.

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