Detection and Tracking of Multiple Moving Vehicles with a UAV

Lee, Min-Hyuck; Yeom, Seokwon

doi:10.5391/ijfis.2018.18.3.182

Cited by 23 publications

(13 citation statements)

References 21 publications

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“…Two-point initialization has been applied to target tracking by a drone [25][26][27]. The initial state of each target is calculated by the two-point differencing following a maximum speed gating.…”

Section: Two Point Intializationmentioning

confidence: 99%

Moving Vehicle Tracking with a Moving Drone Based on Track Association

Yeom

Nam

2021

Applied Sciences

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The drone has played an important role in security and surveillance. However, due to the limited computing power and energy resources, more efficient systems are required for surveillance tasks. In this paper, we address detection and tracking of moving vehicles with a small drone. A moving object detection scheme has been developed based on frame registration and subtraction followed by morphological filtering and false alarm removing. The center position of the detected object area is the input to the tracking target as a measurement. The Kalman filter estimates the position and velocity of the target based on the measurement nearest to the state prediction. We propose a new data association scheme for multiple measurements on a single target. This track association method consists of the hypothesis testing between two tracks and track fusion through track selection and termination. We reduce redundant tracks on the same target and maintain the track with the least estimation error. In the experiment, drones flying at an altitude of 150 m captured two videos in an urban environment. There are a total of 9 and 23 moving vehicles in each video; the detection rates are 92% and 89%, respectively. The number of valid tracks is significantly reduced from 13 to 10 and 56 to 26 in the first and the second video, respectively. In the first video, the average position RMSE of two merged tracks are improved by 83.6% when only the fused states are considered. In the second video, the average position and velocity RMSE are 1.21 m and 1.97 m/s, showing the robustness of the proposed system.

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Section: Two Point Intializationmentioning

confidence: 99%

Moving Vehicle Tracking with a Moving Drone Based on Track Association

Yeom

Nam

2021

Applied Sciences

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“…The track is terminated when the track continuously fails to update its state with validated measurements for a certain number of frames. It is noted that when there is no measurement in the validation region, the track can update its state as Equations (23) and (24), not Equations (21) and (22). The terminated track is also considered false if the number of updates with validated measurements is too small, that is, it is assumed that the true target generates at least a certain number of validated measurements.…”

Section: State Estimate and Covariance Updatementioning

confidence: 99%

“…In this paper, we address the detection and tracking of multiple moving pedestrians by an SUAV or drone. Visual detection is performed through frame subtraction, with thresholding and dilation operation, and false alarm removal [24,25]. Each frame is subtracted from a past frame, separated by a constant interval.…”

Section: Introductionmentioning

confidence: 99%

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

Yeom

Cho

2019

Applied Sciences

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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.

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“…An all-in-one camera-based target detection and positioning system was studied for search and rescue mission in [7]. [8] and [9] studied the detection and tracking of moving vehicles by using Kalman and interacting multiple model (IMM) filters, respectively. The detection and tracking of pedestrians were studied with a drone hovering at a low altitude in [10].In past studies, the drone hovered in one position as a stationary sensor, and the field of view was fixed to capture the same scene.…”

Section: Introductionmentioning

confidence: 99%

Moving Vehicle Detection and Drone Velocity Estimation with a Moving Drone

Nam¹,

Yeom²

2020

IJFIS

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Small unmanned aerial vehicles can be effectively used for aerial video surveillance. Although the field of view of the camera mounted on the drone is limited, flying drones can expand their surveillance coverage. In this paper, we address the detection of moving targets in urban environments with a moving drone. The drone moves at a constant velocity and captures video clips of moving vehicles such as cars, buses, and bicycles. Moving vehicle detection consists of frame registration and subtraction followed by thresholding, morphological operations and false blob reduction. First, two consecutive frames are registered; the coordinates of the next frame are compensated by a displacement vector that minimizes the sum of absolute difference between the two frames. Second, the next compensated frame is subtracted from the current frame, and the binary image is generated by thresholding. Finally, morphological operations and false alarm removal extract the target blobs. In the experiments, the drone flies at a constant speed of 5.1 m/s at an altitude of 150 m while capturing video clips of nine moving targets. The detection and false alarm rates as well as the receiver operating characteristic curves are obtained, and the drone velocities in the x and y directions are estimated by the displacement vector. The average detection rate ranges from 90% to 97% while the false alarm rate ranges from 0.06 to 0.5. The root mean square error of the speed is 0.07 m/s when the reference frame is fixed, showing the robustness of the proposed method.

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Detection and Tracking of Multiple Moving Vehicles with a UAV

Cited by 23 publications

References 21 publications

Moving Vehicle Tracking with a Moving Drone Based on Track Association

Moving Vehicle Tracking with a Moving Drone Based on Track Association

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

Moving Vehicle Detection and Drone Velocity Estimation with a Moving Drone

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