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

Yeom, Seokwon; Cho, In-Jun

doi:10.3390/app9163359

Cited by 26 publications

(19 citation statements)

References 22 publications

(36 reference statements)

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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%

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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%

“…Then, the NN association assigns valid measurements to tracks. However, multiple tracks can be generated if multiple measurements on a single target are detected [27]. In the paper, we propose a new association scheme, track-track association, in order to maintain a single track for each target.…”

Section: Introductionmentioning

confidence: 99%

Moving Vehicle Tracking with a Moving Drone Based on Track Association

Yeom

Nam

2021

Applied Sciences

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“…This type of UAV can perform vertical take-off and landing (VTOL), remain stationary (hover), and fly at a moderate speed. Such abilities make multirotors suitable for a wide range of missions like surveillance [ 1 ], precise agriculture [ 2 ], search and rescue [ 3 ], and many others. Most common (conventional) multirotor configurations consist of an even number of rotors arranged symmetrically in one (or more) parallel planes, such as quadrotor [ 4 ], hexarotor [ 5 ], or octorotor [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

A Modular Multirotor Unmanned Aerial Vehicle Design Approach for Development of an Engineering Education Platform

Kotarski

Piljek

Pranjic

et al. 2021

Sensors

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The development of multirotor unmanned aerial vehicles (UAVs) has enabled a vast number of applications. Since further market growth is expected in the future it is important that modern engineers be familiar with these types of mechatronic systems. In this paper, a comprehensive mathematical description of a multirotor UAV, with various configuration parameters, is given. A modular design approach for the development of an educational multirotor platform is proposed. Through the stages of computer-aided design and rapid prototyping an experimental modular multirotor (EMMR) platform is presented. Open-source control system and a novel EMMR enable students to create and test control algorithms for various multirotor configurations. The presented EMMR platform is suitable for students to expand their educational objectives in aerial robotics and control theory.

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“…[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. The surveillance coverage of the drone depends on its distance to the object and the focal length of the camera.…”

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 Moving Pedestrians with a Small Unmanned Aerial Vehicle

Cited by 26 publications

References 22 publications

Moving Vehicle Tracking with a Moving Drone Based on Track Association

Moving Vehicle Tracking with a Moving Drone Based on Track Association

A Modular Multirotor Unmanned Aerial Vehicle Design Approach for Development of an Engineering Education Platform

Moving Vehicle Detection and Drone Velocity Estimation with a Moving Drone

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