An Efficient Geometric Transformation-Based Approach for Multi-UAV Image Stitching
Received: 25 April 2025 | Revised: 20 May 2025 and 2 June 2025 | Accepted: 8 June 2025 | Online: 2 August 2025
Corresponding author: Ayoub El-alami
Abstract
UAV-based imaging systems often require real-time image stitching capabilities for tasks such as mapping, surveillance, and environmental monitoring. Traditional approaches typically rely on homography-based transformations, which, although robust, are computationally demanding. These methods often require reestimating transformation parameters for each video frame due to continuous camera motion, significantly increasing computational load and making real-time performance challenging, especially in resource-constrained drone applications. This study proposes an optimized image stitching method based on explicit rotation, scaling, and translation transformations derived from predefined anchor points. Designed for scenarios that involve relatively stable and flat scenes with known anchor markers, this approach removes the reliance on complex matrix operations. Evaluation on experimental footage demonstrates that the method reduces computational complexity, achieving processing times approximately 36% of those required by conventional homography-based methods. Visual assessments and alignment accuracy metrics indicate that the proposed method maintains the same alignment quality, making it particularly suitable for real-time UAV operations and embedded vision applications where computational efficiency is critical.
Keywords:
computer vision, image stitching, object detection, object tracking, real-time processingDownloads
References
A. El-Alami, Y. Nadir, and K. Mansouri, "A review of object detection approaches for traffic surveillance systems," International Journal of Electrical and Computer Engineering (IJECE), vol. 14, no. 5, Oct. 2024, Art. no. 5221. DOI: https://doi.org/10.11591/ijece.v14i5.pp5221-5233
W. Luo, J. Xing, A. Milan, X. Zhang, W. Liu, and T. K. Kim, "Multiple object tracking: A literature review," Artificial Intelligence, vol. 293, Apr. 2021, Art. no. 103448. DOI: https://doi.org/10.1016/j.artint.2020.103448
M. S. K. Namana and B. U. Kumar, "Enhancing Surveillance Systems leveraging AIoT for Advanced Object Detection in Real-Time Security Applications," Engineering, Technology & Applied Science Research, vol. 15, no. 3, pp. 22507–22517, Jun. 2025. DOI: https://doi.org/10.48084/etasr.9926
A. El-alami, Y. Nadir, and K. Mansouri, "A Modified Lightweight DeepSORT Variant for Vehicle Tracking," International Journal of Advanced Computer Science and Applications, vol. 15, no. 10, 2024. DOI: https://doi.org/10.14569/IJACSA.2024.0151067
K. Joshi and M. I. Patel, "Recent advances in local feature detector and descriptor: a literature survey," International Journal of Multimedia Information Retrieval, vol. 9, no. 4, pp. 231–247, Dec. 2020. DOI: https://doi.org/10.1007/s13735-020-00200-3
S. K. Sharma, K. Jain, and A. K. Shukla, "A Comparative Analysis of Feature Detectors and Descriptors for Image Stitching," Applied Sciences, vol. 13, no. 10, May 2023, Art. no. 6015. DOI: https://doi.org/10.3390/app13106015
T. Georgiou, Y. Liu, W. Chen, and M. Lew, "A survey of traditional and deep learning-based feature descriptors for high dimensional data in computer vision," International Journal of Multimedia Information Retrieval, vol. 9, no. 3, pp. 135–170, Sep. 2020. DOI: https://doi.org/10.1007/s13735-019-00183-w
H. Kim, W. K. Jung, Y. C. Park, J. W. Lee, and S. H. Ahn, "Broken stitch detection method for sewing operation using CNN feature map and image-processing techniques," Expert Systems with Applications, vol. 188, Feb. 2022, Art. no. 116014. DOI: https://doi.org/10.1016/j.eswa.2021.116014
F. S. Mohammadi, H. Shabani, and M. Zarei, "Fast and robust feature-based stitching algorithm for microscopic images," Scientific Reports, vol. 14, no. 1, Jun. 2024, Art. no. 13304. DOI: https://doi.org/10.1038/s41598-024-61970-y
C. Qin and X. Ran, "Efficient Unsupervised Image Stitching Using Attention Mechanism with Deep Homography Estimation," Computers, Materials and Continua, vol. 79, no. 1, pp. 1319–1334, Apr. 2024. DOI: https://doi.org/10.32604/cmc.2024.048850
W. Pan, A. Li, X. Liu, and Z. Deng, "Unmanned aerial vehicle image stitching based on multi‐region segmentation," IET Image Processing, vol. 18, no. 14, pp. 4607–4622, Dec. 2024. DOI: https://doi.org/10.1049/ipr2.13271
A. Pujol Miró, "Real-time image stitching for automotive 360o vision systems," B.S. Thesis, Universitat Politècnica de Catalunya, 2014.
Q. Jia, X. Feng, Y. Liu, X. Fan, and L. J. Latecki, "Learning Pixel-wise Alignment for Unsupervised Image Stitching," in Proceedings of the 31st ACM International Conference on Multimedia, Jul. 2023, pp. 1392–1400. DOI: https://doi.org/10.1145/3581783.3612298
M. Lin, T. Liu, Y. Li, X. Miao, and C. He, "Image stitching by disparity-guided multi-plane alignment," Signal Processing, vol. 197, Aug. 2022, Art. no. 108534. DOI: https://doi.org/10.1016/j.sigpro.2022.108534
H. Kweon, H. Kim, Y. Kang, Y. Yoon, W. Jeong, and K. J. Yoon, "Pixel-wise Deep Image Stitching." arXiv, Dec. 12, 2021.
A. Li, J. Guo, and Y. Guo, "Image Stitching Based on Semantic Planar Region Consensus," IEEE Transactions on Image Processing, vol. 30, pp. 5545–5558, 2021. DOI: https://doi.org/10.1109/TIP.2021.3086079
T. Liao, C. Wang, L. Li, G. Liu, and N. Li, "Parallax-tolerant Image Stitching via Segmentation-guided Multi-homography Warping." arXiv, 2024. DOI: https://doi.org/10.1016/j.sigpro.2024.109860
L. Nie, C. Lin, K. Liao, M. Liu, and Y. Zhao, "A view-free image stitching network based on global homography," Journal of Visual Communication and Image Representation, vol. 73, Nov. 2020, Art. no. 102950. DOI: https://doi.org/10.1016/j.jvcir.2020.102950
L. Nie, C. Lin, K. Liao, S. Liu, and Y. Zhao, "Unsupervised Deep Image Stitching: Reconstructing Stitched Features to Images," IEEE Transactions on Image Processing, vol. 30, pp. 6184–6197, 2021. DOI: https://doi.org/10.1109/TIP.2021.3092828
S. Bang, H. Kim, and H. Kim, "UAV-based automatic generation of high-resolution panorama at a construction site with a focus on preprocessing for image stitching," Automation in Construction, vol. 84, pp. 70–80, Dec. 2017. DOI: https://doi.org/10.1016/j.autcon.2017.08.031
W. Lyu, Z. Zhou, L. Chen, and Y. Zhou, "A survey on image and video stitching," Virtual Reality & Intelligent Hardware, vol. 1, no. 1, pp. 55–83, Feb. 2019. DOI: https://doi.org/10.3724/SP.J.2096-5796.2018.0008
M. Fu et al., "Image Stitching Techniques Applied to Plane or 3-D Models: A Review," IEEE Sensors Journal, vol. 23, no. 8, pp. 8060–8079, Apr. 2023. DOI: https://doi.org/10.1109/JSEN.2023.3251661
A. El-Alami, "An Efficient geometric based Approach for Real-Time multi UAV Image Stitching," Apr. 03, 2025. https://www.youtube.com/watch?v=7Af9zBixMUA.
A. El-Alami, "Geometric-Stitching." May 20, 2025, [Online]. Available: https://github.com/ayoubalami/Geometric-Stitching.
Downloads
How to Cite
License
Copyright (c) 2025 Ayoub El-alami, Younes Nadir, Khalifa Mansouri
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
