@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 {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.}
}
