Vision-Based Moving UAV Tracking by Another UAV on Low-Cost Hardware and a New Ground Control Station

Cintas, Emre; Ozyer, Baris; Simsek, Emrah

doi:10.1109/access.2020.3033481

Cited by 25 publications

(11 citation statements)

References 33 publications

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“…When the score is under a given threshold, the target’s new position is selected according to Euclidean distances and the IoU ratio between candidates from the detector and the tracker’s most recent output. In addition, Cintas et al [ 36 ] utilized YOLOv3-Tiny and the KCF, and they activate a switch mechanism every 30 frames, or in cases where the tracker fails. Then, based on the Euclidean distance between the predicted results by the detector and the latest result produced by the tracker, the result with the closest location is considered the target.…”

Section: Related Workmentioning

confidence: 99%

“…If the disagreement measure satisfies the threshold, the bounding box of the detector is determined to be the target. Although the approaches in [ 5 , 6 , 36 ] are fast and straightforward, the target is localized using only the bounding box (rather than appearance features); thus, this approach is ineffective at handling the tracking drift problems. Therefore, a sophisticated mechanism is required to track UAVs in real-world situations.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Online Learning-Based Hybrid Tracking Method for Unmanned Aerial Vehicles

Son

Lee

Cha

et al. 2023

Sensors

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Tracking unmanned aerial vehicles (UAVs) in outdoor scenes poses significant challenges due to their dynamic motion, diverse sizes, and changes in appearance. This paper proposes an efficient hybrid tracking method for UAVs, comprising a detector, tracker, and integrator. The integrator combines detection and tracking, and updates the target’s features online while tracking, thereby addressing the aforementioned challenges. The online update mechanism ensures robust tracking by handling object deformation, diverse types of UAVs, and changes in background. We conducted experiments on custom and public UAV datasets to train the deep learning-based detector and evaluate the tracking methods, including the commonly used UAV123 and UAVL datasets, to demonstrate generalizability. The experimental results show the effectiveness and robustness of our proposed method under challenging conditions, such as out-of-view and low-resolution scenarios, and demonstrate its performance in UAV detection tasks.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Online Learning-Based Hybrid Tracking Method for Unmanned Aerial Vehicles

Son

Lee

Cha

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…A relevant context in which the UAVs are utilized regards land monitoring. Technical features of these devices, e.g., high silence and reduced size, make them especially suitable for critical missions [57,58]. In [59], for example, the authors propose a deep learning-based algorithm for tracking objects within a monitoring environment.…”

Section: Introductionmentioning

confidence: 99%

Low-Altitude Aerial Video Surveillance via One-Class SVM Anomaly Detection from Textural Features in UAV Images

et al. 2021

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In recent years, small-scale Unmanned Aerial Vehicles (UAVs) have been used in many video surveillance applications, such as vehicle tracking, border control, dangerous object detection, and many others. Anomaly detection can represent a prerequisite of many of these applications thanks to its ability to identify areas and/or objects of interest without knowing them a priori. In this paper, a One-Class Support Vector Machine (OC-SVM) anomaly detector based on customized Haralick textural features for aerial video surveillance at low-altitude is presented. The use of a One-Class SVM, which is notoriously a lightweight and fast classifier, enables the implementation of real-time systems even when these are embedded in low-computational small-scale UAVs. At the same time, the use of textural features allows a vision-based system to detect micro and macro structures of an analyzed surface, thus allowing the identification of small and large anomalies, respectively. The latter aspect plays a key role in aerial video surveillance at low-altitude, i.e., 6 to 15 m, where the detection of common items, e.g., cars, is as important as the detection of little and undefined objects, e.g., Improvised Explosive Devices (IEDs). Experiments obtained on the UAV Mosaicking and Change Detection (UMCD) dataset show the effectiveness of the proposed system in terms of accuracy, precision, recall, and F1-score, where the model achieves a 100% precision, i.e., never misses an anomaly, but at the expense of a reasonable trade-off in its recall, which still manages to reach up to a 71.23% score. Moreover, when compared to classical Haralick textural features, the model obtains significantly higher performances, i.e., ≈20% on all metrics, further demonstrating the approach effectiveness.

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“…In the above article [1], our aim was to automatically detect and track target moving Unmanned Aerial Vehicle (UAV) [4] by another UAV in real-time applications. For that purpose, we developed a vision-based low-cost hardware system and analyzed the tracking algorithm performances.…”

mentioning

confidence: 99%

“…• In [1], one reference [3] was missing. Two paragraphs which are given as above, indicating the differences between the proposed approach and reference studies [2], [3] for detection and tracking algorithms should be appended in the paper.…”

mentioning

confidence: 99%