@article {Bourja2022994,
	title = {End-to-End Car Make and Model Classification using Compound Scaling and Transfer Learning},
	journal = {International Journal of Advanced Computer Science and Applications},
	volume = {13},
	number = {5},
	year = {2022},
	note = {cited By 0},
	pages = {994-1001},
	abstract = {Recently, Morocco has started to invest in IoT systems to transform our cities into smart cities that will promote economic growth and make life easier for citizens. One of the most vital addition is intelligent transportation systems which represent the foundation of a smart city. However, the problem often faced in such systems is the recognition of entities, in our case, car and model makes. This paper proposes an approach that identifies makes and models for cars using transfer learning and a workflow that first enhances image quality and quantity by data augmentation and then feeds the newly generated data into a deep learning model with a scaling feature{\textendash}that is, compound scaling. In addition, we developed a web interface using the FLASK API to make real-time predictions. The results obtained were 80\% accuracy, fine-tuning it to an accuracy rate of 90\% on unseen data. Our framework is trained on the commonly used Stanford Cars dataset. {\textcopyright} 2022. International Journal of Advanced Computer Science and Applications. All Rights Reserved.},
	keywords = {Application programming interfaces (API), Compound scaling, Deep learning, Economic growths, Economics, End to end, Image enhancement, Intelligent systems, Intelligent transportation systems, Internet of things, IOT, Model classification, Scalings, Smart city, Transfer learning, Vehicle classification},
	doi = {10.14569/IJACSA.2022.01305111},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131410221\&doi=10.14569\%2fIJACSA.2022.01305111\&partnerID=40\&md5=aeb1c5a894ab70005066f491ebf3623c},
	author = {Bourja, O. and Maach, A. and Zannouti, Z. and Derrouz, H. and Mekhzoum, H. and Abdelali, H.A. and Thami, R.O.H. and Bourzeix, F.}
}
@article {Bourja2021915,
	title = {Real Time Vehicle Detection, Tracking, and Inter-vehicle Distance Estimation based on Stereovision and Deep Learning using Yolov3},
	journal = {International Journal of Advanced Computer Science and Applications},
	volume = {12},
	number = {8},
	year = {2021},
	note = {cited By 1},
	pages = {915-923},
	abstract = {Abstract{\textemdash}In this paper, we propose a robust real-time vehicle tracking and inter-vehicle distance estimation algorithm based on stereovision. Traffic images are captured by a stereoscopic system installed on the road, and then we detect moving vehicles with the YOLO V3 Deep Neural Network algorithm. Thus, the real-time video goes through an algorithm for stereoscopy-based measurement in order to estimate distance between detected vehicles. However, detecting the real-time objects have always been a challenging task because of occlusion, scale, illumination etc. Thus, many convolutional neural network models based on object detection were developed in recent years. But they cannot be used for real-time object analysis because of slow speed of recognition. The model which is performing excellent currently is the unified object detection model which is You Only Look Once (YOLO). But in our experiment, we have found that despite of having a very good detection precision, YOLO still has some limitations. YOLO processes every image separately even in a continuous video or frames. Because of this much important identification can be lost. So, after the vehicle detection and tracking, inter-vehicle distance estimation is done. {\textcopyright} 2021. International Journal of Advanced Computer Science and Applications. All Rights Reserved.},
	keywords = {Bounding-box, Convolution, Convolutional neural network, Convolutional neural networks, Deep neural networks, Distance estimation, Estimation algorithm, Object detection, Object recognition, Real- time, Stereo image processing, Stereoimages, Stereovision, Tracking, Vehicles, Vehicles detection, YOLOv3 deep neural network},
	doi = {10.14569/IJACSA.2021.01208101},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118990281\&doi=10.14569\%2fIJACSA.2021.01208101\&partnerID=40\&md5=225926d9445a4fed55a125a55e519b2f},
	author = {Bourja, O. and Derrouz, H. and Abdelali, H.A. and Maach, A. and Thami, R.O.H. and Bourzeix, F.}
}
@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.}
}
@conference {ElBouziady20181,
	title = {Vehicle speed estimation using extracted SURF features from stereo images},
	booktitle = {2018 International Conference on Intelligent Systems and Computer Vision, ISCV 2018},
	volume = {2018-May},
	year = {2018},
	pages = {1-6},
	doi = {10.1109/ISACV.2018.8354040},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050956891\&doi=10.1109\%2fISACV.2018.8354040\&partnerID=40\&md5=53370a3035d03bc86531c0e0e81069f5},
	author = {El Bouziady, A. and Oulad Haj Thami, R. and Ghogho, M. and Bourja, O. and El Fkihi, S.}
}
