@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 {Derrouz20225561,
	title = {End-to-end quantum-inspired method for vehicle classification based on video stream},
	journal = {Neural Computing and Applications},
	volume = {34},
	number = {7},
	year = {2022},
	note = {cited By 1},
	pages = {5561-5576},
	abstract = {Intelligent Transportation Systems (ITS) are the most widely used systems for road traffic management. The vehicle type classification (VTC) is a crucial ITS task due to its capability to gather valuable traffic information. However, designing a performant VTC method is challenging due to the considerable intra-class variation of vehicles. This paper presents a new quantum decision-based method for VTC applied to video streaming. This method allows for earlier decision-making by considering a few stream{\textquoteright}s images. Our method is threefold. First, the video stream is acquired and preprocessed following a specific pipeline. Second, we aim to detect and track vehicles. Therefore, we apply a deep learning-based model to detect vehicles, and then a vehicle tracking algorithm is used to track each detected vehicle. Third, we seek to classify the tracked vehicle according to six defined classes. Furthermore, we transform the tracked vehicles according to a pipeline, consisting of the histogram of oriented gradients (HOG), and principal component analysis (PCA) methods. Then, we estimate the vehicles{\textquoteright} probabilities of belonging to each class by training multilayer perceptron (MLP) classifier with the resulting features. To assign a class to a vehicle, we apply a quantum-inspired probability integrator that handles each frame{\textquoteright}s information flow. The unique characteristics of the work we propose, compared to the existing ones, are expressed in the decision-making process, since the former requires a sequence of frames of different sizes, compared to the image-based-decision made by the other methods. Our method outperformed the baseline methods with an accuracy up to 96\%. {\textcopyright} 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.},
	keywords = {Classification (of information), Decision making, Decisions makings, Deep learning, Early decision, Early decision making, End to end, Intelligent systems, Intelligent transportation systems, Intelligent vehicle highway systems, Pipelines, Principal component analysis, Quantum inspired algorithm, Tracked vehicles, Type classifications, Vehicle classification, Vehicle type classification, Vehicle types, Video streaming},
	doi = {10.1007/s00521-021-06718-9},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122251939\&doi=10.1007\%2fs00521-021-06718-9\&partnerID=40\&md5=8e1d19359f8245b2f82d8558670d4ba1},
	author = {Derrouz, H. and Cabri, A. and Ait Abdelali, H. and Oulad Haj Thami, R. and Bourzeix, F. and Rovetta, S. and Masulli, F.}
}
@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 {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.}
}
@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.}
}
