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Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark
Open accessAbstract
Unmanned Aerial Vehicles (UAV) can pose a major risk for aviation safety, due to both negligent and malicious use. For this reason, the automated detection and tracking of UAV is a fundamental task in aerial security systems. Common technologies for UAV detection include visible-band and thermal infrared imaging, radio frequency and radar. Recent advances in deep neural networks (DNNs) for image-based object detection open the possibility to use visual information for this detection and tracking task. Furthermore, these detection architectures can be implemented as backbones for visual tracking systems, thereby enabling persistent tracking of UAV incursions. To date, no comprehensive performance benchmark exists that applies DNNs to visible-band imagery for UAV detection and tracking. To this end, three datasets with varied environmental conditions for UAV detection and tracking, comprising a total of 241 videos (331,486 images), are assessed using four detection architectures and three tracking frameworks. The best performing detector architecture obtains an mAP of 98.6% and the best performing tracking framework obtains a MOTA of 98.7%. Cross-modality evaluation is carried out between visible and infrared spectrums, achieving a maximal 82.8% mAP on visible images when training in the infrared modality. These results provide the first public multi-approach benchmark for state-of-the-art deep learning-based methods and give insight into which detection and tracking architectures are effective in the UAV domain.
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Counter-drone technology is widely under study, which for example enables configuration of guarded areas to either permit or block drones.
“To prevent those misuses as in [150-154], counter-drone technology is widely under study, which for example, enables the configuration of guarded areas to either permit or block drones.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [153]
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
(2016) · Cited by 31,590
Feature Pyramid Networks for Object Detection
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105
SSD: Single Shot MultiBox Detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_2 · Cited by 22,128
End-to-End Object Detection with Transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58452-8_13 · Cited by 18,285
YOLOv3: An Incremental Improvement
Redmon, J., & Farhadi, A. (2018) · arXiv · doi:10.48550/arxiv.1804.02767 · Cited by 17,141
The Hungarian method for the assignment problem
Kuhn, H. W. (1955) · Naval Research Logistics Quarterly · doi:10.1002/nav.3800020109 · Cited by 13,120
Decoupled Weight Decay Regularization
Loshchilov, I., & Hutter, F. (2017) · arXiv · doi:10.48550/arxiv.1711.05101 · Cited by 12,522
Object Detection With Deep Learning: A Review
Zhao, Z.-Q., Zheng, P., Xu, S.-T., & Wu, X. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2876865 · Cited by 5,545
Simple online and realtime tracking with a deep association metric
Wojke, N., Bewley, A., & Paulus, D. (2017) · 2017 IEEE International Conference on Image Processing (ICIP) · doi:10.1109/icip.2017.8296962 · Cited by 5,286
R-FCN: Object Detection via Region-based Fully Convolutional NetworksPreprint
Dai, J., Li, Y., He, K., & Sun, J. Z. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.06409 · Cited by 5,135
CONDENSATION—Conditional Density Propagation for Visual Tracking
Isard, M., & Blake, A. (1998) · International Journal of Computer Vision · doi:10.1023/a:1008078328650 · Cited by 4,886
Simple online and realtime tracking
Bewley, A., Ge, Z., Ott, L., Ramos, F., & Upcroft, B. (2016) · 2016 IEEE International Conference on Image Processing (ICIP) · doi:10.1109/icip.2016.7533003 · Cited by 4,433
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.03385 · Cited by 4,196
CornerNet: Detecting Objects as Paired Keypoints
Law, H., & Deng, J. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01264-9_45 · Cited by 3,852
Performance Measures and a Data Set for Multi-target, Multi-camera Tracking
Ristani, E., Solera, F., Zou, R., Cucchiara, R., & Tomasi, C. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-48881-3_2 · Cited by 3,104
Deep Learning for Generic Object Detection: A Survey
Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01247-4 · Cited by 2,965
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., & LeCun, Y. (2013) · arXiv · doi:10.48550/arxiv.1312.6229 · Cited by 2,680
SiamRPN++: Evolution of Siamese Visual Tracking With Very Deep Networks
Li, B., Wu, W., Wang, Q., Zhang, F., Xing, J., & Yan, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00441 · Cited by 2,607
MMDetection: Open MMLab Detection Toolbox and Benchmark
Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., Xu, J., et al. (2019) · arXiv · doi:10.48550/arxiv.1906.07155 · Cited by 2,227
Avidan, S. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.35 · Cited by 1,119
Tracking Without Bells and Whistles
Bergmann, P., Meinhardt, T., & Leal-Taixe, L. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00103 · Cited by 1,110
Learning to Track: Online Multi-object Tracking by Decision Making
Yu, X., Alahi, A., & Savarese, S. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.534 · Cited by 684
Deep learning in video multi-object tracking: A survey
Ciaparrone, G., Luque Sánchez, F., Tabik, S., Troiano, L., Tagliaferri, R., & Herrera, F. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.11.023 · Cited by 665
The Hungarian Method for the Assignment Problem
Kuhn, H. W. (2009) · Springer eBooks · doi:10.1007/978-3-540-68279-0_2 · Cited by 454
Bae, S., & Yoon, K. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.159 · Cited by 417
Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor
Choi, W. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.347 · Cited by 409
3D Traffic Scene Understanding From Movable Platforms
Geiger, A., Lauer, M., Wojek, C., Stiller, C., & Urtasun, R. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.185 · Cited by 397
Machine Learning-Based Drone Detection and Classification: State-of-the-Art in Research
Taha, B., & Shoufan, A. (2019) · IEEE Access · doi:10.1109/access.2019.2942944 · Cited by 375
Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study
Yang, W., Yuan, Y., Ren, W., Liu, J., Scheirer, W. J., Wang, Z., Zhang, T., Zhong, Q., Xie, D., Pu, S., et al. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2981922 · Cited by 348
Defending Airports from UAS: A Survey on Cyber-Attacks and Counter-Drone Sensing Technologies
Lykou, G., Moustakas, D., & Gritzalis, D. (2020) · Sensors · doi:10.3390/s20123537 · Cited by 268
Deep learning-based strategies for the detection and tracking of drones using several cameras
Unlu, E., Zenou, E., Riviere, N., & Dupouy, P.-É. (2019) · IPSJ Transactions on Computer Vision and Applications · doi:10.1186/s41074-019-0059-x · Cited by 179
Visual tracking using learned linear subspaces
Ho, J., Lee, K.-C., Yang, M., & Kriegman, D. J. (2004) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2004.1315111 · Cited by 162
Stiefelhagen, R., Bernardin, K., Bowers, R., Garofolo, J. S., Mostefa, D., & Soundararajan, P. (2007) · Springer eBooks · doi:10.1007/978-3-540-69568-4_1 · Cited by 159
Empirical study of drone sound detection in real-life environment with deep neural networks
Jeon, S., Shin, J.-W., Lee, Y., Kim, W.-H., Kwon, Y., & Yang, H.-Y. (2017) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco.2017.8081531 · Cited by 154
Anti-UAV: A Large Multi-Modal Benchmark for UAV TrackingPreprint
Jiang, N., Wang, K., Peng, X., Yu, X., Wang, Q., Xing, J., Li, G., Zhao, J. C., Guo, G., & Han, Z. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.08466 · Cited by 126
Flying objects detection from a single moving camera
Rozantsev, A., Lepetit, V., & Fua, P. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7299040 · Cited by 125
Drone Detection in Long-Range Surveillance Videos
Nalamati, M., Kapoor, A., Saqib, M., Sharma, N., & Blumenstein, M. (2019) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2019.8909830 · Cited by 116
UAV Detection System with Multiple Acoustic Nodes Using Machine Learning Models
Yang, B., Matson, E. T., Smith, A. H., Dietz, J. E., & Gallagher, J. C. (2019) · IEEE International Conference on Robotic Computing (IRC) · doi:10.1109/irc.2019.00103 · Cited by 97
Flying Objects Detection from a Single Moving CameraPreprint
Rozantsev, A., Lepetit, V., & Fua, P. (2016) · Cited by 85
Object Tracking Methods:A Review
Soleimanitaleb, Z., Keyvanrad, M. A., & Jafari, A. (2019) · International Conference on Computer and Knowledge Engineering · doi:10.1109/iccke48569.2019.8964761 · Cited by 76
Temporal dynamic appearance modeling for online multi-person tracking
Yang, M., & Jia, Y. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.05.003 · Cited by 71
Drone-vs-Bird Detection Challenge at IEEE AVSS2019
Coluccia, A., Fascista, A., Schumann, A., Sommer, L. W., Ghenescu, M. T., Piatrik, T., Cubber, G. D., Nalamati, M., Kapoor, A., Saqib, M., et al. (2019) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2019.8909876 · Cited by 68
Klare, J., Biallawons, O., & Cerutti‐Maori, D. (2017) · International Radar Symposium · doi:10.23919/irs.2017.8008140 · Cited by 65
Spatio-Temporal Semantic Segmentation for Drone Detection
Craye, C., & Ardjoune, S. (2019) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2019.8909854 · Cited by 62
Robust Detection of Micro-UAS Drones with L-Band 3-D Holographic Radar
Jahangir, M., & Baker, C. (2016) · Sensor Signal Processing for Defence (SSPD) · doi:10.1109/sspd.2016.7590610 · Cited by 52
Real-time UAV Detection based on Deep Learning Network
Hassan, S. A., Rahim, T., & Shin, S. Y. (2019) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc46691.2019.8939564 · Cited by 49
Micro-UAV Detection and Identification Based on Radio Frequency Signature
Xiao, Y., & Zhang, X. (2019) · International Conference on Systems and Informatics (ICSAI) · doi:10.1109/icsai48974.2019.9010185 · Cited by 47
Adaptive Inattentional Framework for Video Object Detection With Reward-Conditional Training
Rodríguez-Ramos, A., Rodríguez-Vázquez, J., Sampedro, C., & Campoy, P. (2020) · IEEE Access · doi:10.1109/access.2020.3006191 · Cited by 37
Detecting drones using machine learning
Scheller, W. D. (2017) · doi:10.31274/etd-180810-5839 · Cited by 14
Video Surveillance using Deep Learning - A Review
Shana, L., & Christopher, C. S. (2019) · International Conference on Recent Advances in Energy-efficient Computing and Communication (ICRAECC) · doi:10.1109/icraecc43874.2019.8995084 · Cited by 7
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A Survey of Video Surveillance Systems in Smart City
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Wang, J., Dong, Y., Zhao, S., & Zhang, Z. (2023) · Sensors · doi:10.3390/s23020724 · Cited by 60
“Vehicle Tracking With the help of vehicle models, the multi-target vehicle tracking technology based on CNN has also attracted much attention [31].”
BackgroundZitar, R. A., Mohsen, A., Seghrouchni, A. E. F., Barbaresco, F., & Al-Dmour, N. A. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-023-09894-0 · Cited by 55
Yasmine, G., Gmira, M., & Medromi, H. (2024) · Applied Artificial Intelligence · doi:10.1080/08839514.2024.2318672 · Cited by 26
Yasmine, G., Gmira, M., & Medromi, H. (2023) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2023.200296 · Cited by 22
Detecting infrared UAVs on edge devices through lightweight instance segmentation
Chen, Y., Sun, H., Tian, L., Yang, Y., Wang, S., & Wang, T. (2025) · PLoS ONE · doi:10.1371/journal.pone.0330074 · Cited by 8
Bird/Drone Detection and Classification using Classical and Deep Learning MethodsPreprint
Zitar, R. A., Kassab, M., Seghrouchni, A. E. F., & Barbaresco, F. (2023) · doi:10.22541/au.168075364.45332093/v1 · Cited by 7
Computer Vision Application in Object Detection and Tracking for Aerial Surveillance
Nandibewoor, A., Prateek, L. K., Sakaray, M., Hassan, A., Ravankar, A., & Hegde, A. (2023) · Indian Journal of Science and Technology · doi:10.17485/ijst/v16i31.645 · Cited by 1
журналу, Р. (2024) · Збірник наукових праць Національної академії Державної прикордонної служби України Серія військові та технічні науки · doi:10.32453/3.v92i3.1572
МАТЕМАТИЧНА МОДЕЛЬ ВИЗНАЧЕННЯ ВІЗУАЛЬНОЇ ДАЛЬНОСТІ ВИЯВЛЕННЯ БЕЗПІЛОТНОГО ЛІТАЛЬНОГО АПАРАТА
ЛИСИЙ, М., БАШИНСЬКИЙ, А., КУПЕЛЬСЬКИЙ, В., КІРЕТОВ, В., & ЛИСИЙ, А. (2024) · Збірник наукових праць Національної академії Державної прикордонної служби України Серія військові та технічні науки · doi:10.32453/3.v92i3.1466
Efficient Tensor Compression and Reconstruction in Split DNNs for Edge-Based Object Detection
Yu, Y., Mendula, M., Levorato, M., Papatriantafilou, M., & Chiasserini, C. F. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3658835
Metadata
- Authors
- Isaac-Medina, B. K. S., Poyser, M., Organisciak, D., Willcocks, C. G., Breckon, T. P., & Shum, H. P. H.
- Affiliations
- Durham University (GB)
- Northumbria University (GB)
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 1223-1232
- ISSN
- 2473-9936 (ISSN-L)
- OpenAlex
- W3138033608
- Cited by
- 9
- References
- 57
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 3 · 2023: 4
- FWCI
- 0.57
- Type
- conference paper
- Language
- en
- Open access
- Licence
- Locations
- IEEE International Conference on Computer Vision Workshops (publishedVersion)
- Northumbria Research Link (Northumbria University) (acceptedVersion)
- Durham Research Online (Durham University) (acceptedVersion)
- Durham Research Online (Durham University) (submittedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Aerospace Engineering › Aerospace Engineering and Control Systems
- Keywords
- unmanned aerial vehicle detection, UAV tracking, deep neural network, object detection, visual object tracking, benchmark, MOTA, maps
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