Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Abdollahzadeh, S.; Allili, M. S.; Boulmerka, A.; Lapointe, J. -F.
A Vision-Based Framework for Safe Landing Zone Mapping of UAVs in Dynamic Environments Journal Article
In: IEEE Open Journal of the Computer Society, vol. 7, pp. 492–503, 2026, ISSN: 26441268 (ISSN).
Abstract | Links | BibTeX | Tags: Aerial vehicle, Air navigation, Aircraft detection, Aircraft landing, Antennas, automatic UAV navigation, Computer vision, Dynamic environments, Forecasting, Homographies, Landing zones, Learning systems, Motion tracking, Object detection, Object recognition, Object Tracking, object trajectory prediction, Robotics, Safe landing, Safe landing zone, safe landing zones (SLZ), Semantic segmentation, Semantics, Trajectories, Trajectory forecasting, Uncrewed aerial vehicles (UAVs), Unmanned aerial vehicle, Unmanned aerial vehicles (UAV)
@article{abdollahzadeh_vision-based_2026,
title = {A Vision-Based Framework for Safe Landing Zone Mapping of UAVs in Dynamic Environments},
author = {S. Abdollahzadeh and M. S. Allili and A. Boulmerka and J. -F. Lapointe},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105029942397&doi=10.1109%2FOJCS.2026.3663268&partnerID=40&md5=b11484e035458c84b1d3f6780b92c91c},
doi = {10.1109/OJCS.2026.3663268},
issn = {26441268 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {IEEE Open Journal of the Computer Society},
volume = {7},
pages = {492–503},
abstract = {Identification safe landing zones (SLZ) for Uncrewed Aerial Vehicles (UAVs) is important to ensure reliable and safe navigation, especially when they are operated in complex and safety-critical environments. However, this is a challenging task due to obstacles and UAV motion. This paper proposes a vision-based framework that maps SLZs in dynamic scenes by integrating several functionalities for analyzing visually static and dynamic aspects of a scene. Static analysis is achieved through context-aware segmentation which divides the image into thematic classes enabling to identify suitable landing surfaces (e.g., roads, grass). For dynamic content analysis, we combine object detection, tracking, and trajectory prediction to determine object occupancy and identify regions free of obstacles. Trajectory prediction is performed through a novel encoder–decoder architecture taking past object positions to predict the most likely future locations. To ensure stable and robust trajectory prediction, we introduce an optimized homography computation using multi-scale image analysis and cumulative updates to compensate UAV motion. We tested our framework on different operational scenarios, including urban and natural scenes with moving objects like vehicles and pedestrians. Obtained results demonstrate its strong performance, and its significant potential for enabling autonomous and safe UAV navigation. © 2020 IEEE.},
keywords = {Aerial vehicle, Air navigation, Aircraft detection, Aircraft landing, Antennas, automatic UAV navigation, Computer vision, Dynamic environments, Forecasting, Homographies, Landing zones, Learning systems, Motion tracking, Object detection, Object recognition, Object Tracking, object trajectory prediction, Robotics, Safe landing, Safe landing zone, safe landing zones (SLZ), Semantic segmentation, Semantics, Trajectories, Trajectory forecasting, Uncrewed aerial vehicles (UAVs), Unmanned aerial vehicle, Unmanned aerial vehicles (UAV)},
pubstate = {published},
tppubtype = {article}
}
Identification safe landing zones (SLZ) for Uncrewed Aerial Vehicles (UAVs) is important to ensure reliable and safe navigation, especially when they are operated in complex and safety-critical environments. However, this is a challenging task due to obstacles and UAV motion. This paper proposes a vision-based framework that maps SLZs in dynamic scenes by integrating several functionalities for analyzing visually static and dynamic aspects of a scene. Static analysis is achieved through context-aware segmentation which divides the image into thematic classes enabling to identify suitable landing surfaces (e.g., roads, grass). For dynamic content analysis, we combine object detection, tracking, and trajectory prediction to determine object occupancy and identify regions free of obstacles. Trajectory prediction is performed through a novel encoder–decoder architecture taking past object positions to predict the most likely future locations. To ensure stable and robust trajectory prediction, we introduce an optimized homography computation using multi-scale image analysis and cumulative updates to compensate UAV motion. We tested our framework on different operational scenarios, including urban and natural scenes with moving objects like vehicles and pedestrians. Obtained results demonstrate its strong performance, and its significant potential for enabling autonomous and safe UAV navigation. © 2020 IEEE.
2.
Allili, M. S.
Effective object tracking by matching object and background models using active contours Proceedings Article
In: Proceedings - International Conference on Image Processing, ICIP, pp. 873–876, IEEE Computer Society, Cairo, 2009, ISBN: 15224880 (ISSN); 978-142445654-3 (ISBN), (Journal Abbreviation: Proc. Int. Conf. Image Process. ICIP).
Abstract | Links | BibTeX | Tags: Active contours, Algorithms, Background model, EM algorithm, EM algorithms, Finite mixture models, Image matching, Image processing, Imaging systems, Mathematical models, Object contour, Object Tracking
@inproceedings{allili_effective_2009,
title = {Effective object tracking by matching object and background models using active contours},
author = {M. S. Allili},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77951940408&doi=10.1109%2fICIP.2009.5414279&partnerID=40&md5=6838bb85dbef6c9548684a506df3d2b2},
doi = {10.1109/ICIP.2009.5414279},
isbn = {15224880 (ISSN); 978-142445654-3 (ISBN)},
year = {2009},
date = {2009-01-01},
booktitle = {Proceedings - International Conference on Image Processing, ICIP},
pages = {873–876},
publisher = {IEEE Computer Society},
address = {Cairo},
abstract = {In this paper, we propose an effective approach for tracking distribution of objects. The approach uses a competition between a tracked objet and background distributions using active contours. Only the segmentation of the object in the first frame is required for initialization. We evolve the object contour by assigning pixels in a fashion that maximizes the likelihood of the object versus the background. This maximization is implemented using an EM-like algorithm, which evolves the object contour exactly to its boundaries, and adapts the parameters of the object and background distributions. ©2009 IEEE.},
note = {Journal Abbreviation: Proc. Int. Conf. Image Process. ICIP},
keywords = {Active contours, Algorithms, Background model, EM algorithm, EM algorithms, Finite mixture models, Image matching, Image processing, Imaging systems, Mathematical models, Object contour, Object Tracking},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we propose an effective approach for tracking distribution of objects. The approach uses a competition between a tracked objet and background distributions using active contours. Only the segmentation of the object in the first frame is required for initialization. We evolve the object contour by assigning pixels in a fashion that maximizes the likelihood of the object versus the background. This maximization is implemented using an EM-like algorithm, which evolves the object contour exactly to its boundaries, and adapts the parameters of the object and background distributions. ©2009 IEEE.
3.
Allili, M. S.; Ziou, D.
A robust video object tracking by using active contours Proceedings Article
In: 2006 Conference on Computer Vision and Pattern Recognition Workshops, pp. 135, IEEE Computer Society, New York, NY, 2006, ISBN: 0769526462 (ISBN); 978-076952646-1 (ISBN), (Journal Abbreviation: Conf. Comput. Vision Pattern Recog. Workshops).
Abstract | Links | BibTeX | Tags: Boundary, Boundary localization, Color, Feature distribution, Image processing, Image segmentation, Kullback-Leibler distance, Level sets, Mathematical models, Mixture of pdfs, Object recognition, Object Tracking, Texture, Tracking (position), Variational techniques, Video object tracking
@inproceedings{allili_robust_2006,
title = {A robust video object tracking by using active contours},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-33845513941&doi=10.1109%2fCVPRW.2006.20&partnerID=40&md5=64ff2be5c45a6c206420bf6eb5589bca},
doi = {10.1109/CVPRW.2006.20},
isbn = {0769526462 (ISBN); 978-076952646-1 (ISBN)},
year = {2006},
date = {2006-01-01},
booktitle = {2006 Conference on Computer Vision and Pattern Recognition Workshops},
volume = {2006},
pages = {135},
publisher = {IEEE Computer Society},
address = {New York, NY},
abstract = {In this paper, we propose a novel object tracking algorithm in video sequences. The formulation of our tracking model is based on variational calculus, where region and boundary information cooperate for object boundary localization by using active contours. In the approach, only the segmentation of the objects in the first frame is required for initialization. The evolution of the object contours on a current frame aims to find the boundary of the objects by minimizing the Kullback-Leibler distance of the region feature s distribution in the vicinity of the contour to the objects versus the background respectively. We show the effectiveness of the approach on examples of object tracking performed on real video sequences. © 2006 IEEE.},
note = {Journal Abbreviation: Conf. Comput. Vision Pattern Recog. Workshops},
keywords = {Boundary, Boundary localization, Color, Feature distribution, Image processing, Image segmentation, Kullback-Leibler distance, Level sets, Mathematical models, Mixture of pdfs, Object recognition, Object Tracking, Texture, Tracking (position), Variational techniques, Video object tracking},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we propose a novel object tracking algorithm in video sequences. The formulation of our tracking model is based on variational calculus, where region and boundary information cooperate for object boundary localization by using active contours. In the approach, only the segmentation of the objects in the first frame is required for initialization. The evolution of the object contours on a current frame aims to find the boundary of the objects by minimizing the Kullback-Leibler distance of the region feature s distribution in the vicinity of the contour to the objects versus the background respectively. We show the effectiveness of the approach on examples of object tracking performed on real video sequences. © 2006 IEEE.