A Survey of Indoor and Outdoor UAV-Based Target Tracking Systems: Current Status, Challenges, Technologies, and Future Directions

Alhafnawi, Mohannad; Masadeh, Ala’eddin; Al‐Obiedollah, Haitham; Ayyash, Moussa; El-Khazali, Reyad; Elgala, Hany

doi:10.1109/access.2023.3292302

Cited by 29 publications

(8 citation statements)

References 109 publications

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“…The value of lightweight unmanned aerial vehicle (UAV) applications continues to increase with new developments in control systems and sensing systems. With the advantages of small size and flexible operation, lightweight UAVs can operate in cluttered and narrow environments, which is more conducive to the execution of ground target reconnaissance and search tasks in near-earth scenarios and is widely used in urban scene monitoring [ 1 , 2 , 3 ], wilderness searching and locating [ 4 , 5 , 6 ], and other application scenarios requiring real-time target detection and localization [ 7 , 8 , 9 ]. Among them, in order to improve the detection efficiency, the use of panoramic sensors can obtain all the ground environment information at a certain moment and can realize the function of real-time output of target position information to improve the utilization value of information.…”

Section: Introductionmentioning

confidence: 99%

Omni-OTPE: Omnidirectional Optimal Real-Time Ground Target Position Estimation System for Moving Lightweight Unmanned Aerial Vehicle

Ding,

Che,

Zhou

et al. 2024

Sensors

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Ground target detection and positioning systems based on lightweight unmanned aerial vehicles (UAVs) are increasing in value for aerial reconnaissance and surveillance. However, the current method for estimating the target’s position is limited by the field of view angle, rendering it challenging to fulfill the demands of a real-time omnidirectional reconnaissance operation. To address this issue, we propose an Omnidirectional Optimal Real-Time Ground Target Position Estimation System (Omni-OTPE) that utilizes a fisheye camera and LiDAR sensors. The object of interest is first identified in the fisheye image, and then, the image-based target position is obtained by solving using the fisheye projection model and the target center extraction algorithm based on the detected edge information. Next, the LiDAR’s real-time point cloud data are filtered based on position–direction constraints using the image-based target position information. This step allows for the determination of point cloud clusters that are relevant to the characterization of the target’s position information. Finally, the target positions obtained from the two methods are fused using an optimal Kalman fuser to obtain the optimal target position information. In order to evaluate the positioning accuracy, we designed a hardware and software setup, mounted on a lightweight UAV, and tested it in a real scenario. The experimental results validate that our method exhibits significant advantages over traditional methods and achieves a real-time high-performance ground target position estimation function.

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Section: Introductionmentioning

confidence: 99%

Omni-OTPE: Omnidirectional Optimal Real-Time Ground Target Position Estimation System for Moving Lightweight Unmanned Aerial Vehicle

Ding,

Che,

Zhou

et al. 2024

Sensors

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“…While existing algorithms have made strides in pedestrian tracking [12], the demand for more robust and adaptable solutions remains. Our research addresses this demand by leveraging advanced deep learning techniques and integrating them into a unified framework, StrongSORT.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Pedestrian Tracking in Autonomous Vehicles by Using Advanced Deep Learning Techniques

Sukkar,

Shukla,

Kumar

et al. 2024

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Effective collision risk reduction in autonomous vehicles relies on robust and straightforward pedestrian tracking. Challenges posed by occlusion and switching scenarios significantly impede the reliability of pedestrian tracking. In the current study, we strive to enhance the reliability and also the efficacy of pedestrian tracking in complex scenarios. Particularly, we introduce a new pedestrian tracking algorithm that leverages both the YOLOv8 (You Only Look Once) object detector technique and the StrongSORT algorithm, which is an advanced deep learning multi-object tracking (MOT) method. Our findings demonstrate that StrongSORT, an enhanced version of the DeepSORT MOT algorithm, substantially improves tracking accuracy through meticulous hyperparameter tuning. Overall, the experimental results reveal that the proposed algorithm is an effective and efficient method for pedestrian tracking, particularly in complex scenarios encountered in the MOT16 and MOT17 datasets. The combined use of Yolov8 and StrongSORT contributes to enhanced tracking results, emphasizing the synergistic relationship between detection and tracking modules.

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“…Unmanned aerial vehicles (UAVs), also known as drones, are utilized in IoT smart gadgets to gather data from multiple sources in sectors such as military, agriculture, parking, and more [1,2]. UAVs can be categorized into multirotor systems and fixed-wing systems, with the latter being more commonly used [3,4]. Fixed-wing aircraft generate lift through static wings and forward velocity, while rotorcraft, including multirotor systems, generate lift using rotating wings.…”

Section: Introductionmentioning

confidence: 99%

UAV-Based Secure Data Communication: Multilevel Authentication Perspective

Aljumah

2024

Sensors

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An Internet of Things (IoT) system for managing and coordinating unmanned aerial vehicles (UAVs) has revolutionized the industrial sector. The largest issue with the design of the Internet of UAVs (IoUAV) is security. Conspicuously, the novel contribution of the proposed work is to develop a layered authentication approach to facilitate safe IoUAV communication. Specifically, four modules, including the pre-deployment module, user registration module, login module, and authentication module, form the basis of security analysis. In the proposed technique, UAVs are added to the IoUAV registry. The next step is the user registration module, where people are registered with the UAV so they may access the information in real time. In the login module, the user connects with the server for data transmission. Finally, in the authentication module, all entities, including users, servers, and UAVs, are authenticated to ensure secure data communication. The proposed method achieves peak performance as compared to the state-of-the-art techniques in terms of statistical parameters of latency (3.255s), throughput (90.15%), and packet loss (8.854%).

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A Survey of Indoor and Outdoor UAV-Based Target Tracking Systems: Current Status, Challenges, Technologies, and Future Directions

Cited by 29 publications

References 109 publications

Omni-OTPE: Omnidirectional Optimal Real-Time Ground Target Position Estimation System for Moving Lightweight Unmanned Aerial Vehicle

Omni-OTPE: Omnidirectional Optimal Real-Time Ground Target Position Estimation System for Moving Lightweight Unmanned Aerial Vehicle

Enhancing Pedestrian Tracking in Autonomous Vehicles by Using Advanced Deep Learning Techniques

UAV-Based Secure Data Communication: Multilevel Authentication Perspective

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