@article {Abdelali2021164282,
	title = {Multiple hypothesis detection and tracking using deep learning for video traffic surveillance},
	journal = {IEEE Access},
	volume = {9},
	year = {2021},
	note = {cited By 3},
	pages = {164282-164291},
	abstract = {Moroccan Intelligent Transport System is the first Moroccan system that uses the latest advances in computer vision, machine learning and deep learning techniques to manage Moroccan traffic and road violations.In this paper, we propose a fully automatic approach to Multiple Hypothesis Detection and Tracking (MHDT) for video traffic surveillance.The proposed framework combines Kalman filter and data association-based tracking methods using YOLO detection approach, to robustly track vehicles in complex traffic surveillance scenes.Experimental results demonstrate that the proposed approach is robust to detect and track the trajectory of the vehicles in different situations such as scale variation, stopped vehicles, rotation, varying illumination and occlusion.The proposed approach shows a competitive results (detection: 94.10\% accuracy, tracking: 92.50\% accuracy) compared to the state-of-the-art approaches. {\textcopyright} 2021 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.},
	keywords = {Bandpass filters, Computer vision, Convolutional neural network, Data association, Deep learning, Deep neural networks, Detection, Intelligent systems, Intelligent vehicle highway systems, Kalman filters, Monitoring, Multiple hypothesis tracking, Object detection, Occlusion handling, Predictive models, Security systems, Target tracking, Targets tracking, Traffic control, Traffic surveillance, Vehicles, Video sequences},
	doi = {10.1109/ACCESS.2021.3133529},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121370415\&doi=10.1109\%2fACCESS.2021.3133529\&partnerID=40\&md5=84b2a2537c16a7db00901d13c8b6eb5c},
	author = {Abdelali, H.A.I.T. and Derrouz, H. and Zennayi, Y. and Thami, R.O.H. and Bourzeix, F.}
}
@article {Moussaid2021,
	title = {Tree crowns segmentation and classification in overlapping orchards based on satellite images and unsupervised learning algorithms},
	journal = {Journal of Imaging},
	volume = {7},
	number = {11},
	year = {2021},
	note = {cited By 2},
	abstract = {Smart agriculture is a new concept that combines agriculture and new technologies to improve the yield{\textquoteright}s quality and quantity as well as facilitate many tasks for farmers in managing orchards. An essential factor in smart agriculture is tree crown segmentation, which helps farmers automatically monitor their orchards and get information about each tree. However, one of the main problems, in this case, is when the trees are close to each other, which means that it would be difficult for the algorithm to delineate the crowns correctly. This paper used satellite images and machine learning algorithms to segment and classify trees in overlapping orchards. The data used are images from the Moroccan Mohammed VI satellite, and the study region is the OUARGHA citrus orchard located in Morocco. Our approach starts by segmenting the rows inside the parcel and finding all the trees there, getting their canopies, and classifying them by size. In general, the model inputs the parcel{\textquoteright}s image and other field measurements to classify the trees into three classes: missing/weak, normal, or big. Finally, the results are visualized in a map containing all the trees with their classes. For the results, we obtained a score of 0.93 F-measure in rows segmentation. Additionally, several field comparisons were performed to validate the classification, dozens of trees were compared and the results were very good. This paper aims to help farmers to quickly and automatically classify trees by crown size, even if there are overlapping orchards, in order to easily monitor each tree{\textquoteright}s health and understand the tree{\textquoteright}s distribution in the field. {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.},
	doi = {10.3390/jimaging7110241},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119836347\&doi=10.3390\%2fjimaging7110241\&partnerID=40\&md5=28a65e4b68b078f94a4b0144d630f8d8},
	author = {Moussaid, A. and El Fkihi, S. and Zennayi, Y.}
}
@article {AitAbdelali2021517,
	title = {Visual Vehicle Tracking via Deep Learning and Particle Filter},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {1188},
	year = {2021},
	note = {cited By 2},
	pages = {517-526},
	abstract = {Visual vehicle tracking is one of the most challenging research topics in computer vision. In this paper, we propose a novel and efficient approach based on the particle filter technique and deep learning for multiple vehicle tracking, where the main focus is to associate vehicles efficiently for online and real-time applications. Experimental results illustrate the effectiveness of the system we are proposing. {\textcopyright} 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.},
	keywords = {Deep learning, Monte Carlo methods, Particle filter, Real-time application, Research topics, Soft computing, Vehicles},
	doi = {10.1007/978-981-15-6048-4_45},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096559933\&doi=10.1007\%2f978-981-15-6048-4_45\&partnerID=40\&md5=51a9b5456004a8cdb4bc0a5256e17dc9},
	author = {Ait Abdelali, H. and Bourja, O. and Haouari, R. and Derrouz, H. and Zennayi, Y. and Bourzex, F. and Oulad Haj Thami, R.}
}
@conference {Bourja2018502,
	title = {MoVITS: Moroccan Video Intelligent Transport System},
	booktitle = {Colloquium in Information Science and Technology, CIST},
	volume = {2018-October},
	year = {2018},
	pages = {502-507},
	doi = {10.1109/CIST.2018.8596566},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061428090\&doi=10.1109\%2fCIST.2018.8596566\&partnerID=40\&md5=d3b7baffb731883e8897b8cc5356e33e},
	author = {Bourja, O. and Kabbaj, K. and Derrouz, H. and El Bouziady, A. and Thami, R.O.H. and Zennayi, Y. and Bourzeix, F.}
}
