@article {Iazzi2021,
	title = {Fall detection system-based posture-recognition for indoor environments},
	journal = {Journal of Imaging},
	volume = {7},
	number = {3},
	year = {2021},
	note = {cited By 7},
	abstract = {The majority of the senior population lives alone at home. Falls can cause serious injuries, such as fractures or head injuries. These injuries can be an obstacle for a person to move around and normally practice his daily activities. Some of these injuries can lead to a risk of death if not handled urgently. In this paper, we propose a fall detection system for elderly people based on their postures. The postures are recognized from the human silhouette which is an advantage to preserve the privacy of the elderly. The effectiveness of our approach is demonstrated on two well-known datasets for human posture classification and three public datasets for fall detection, using a Support-Vector Machine (SVM) classifier. The experimental results show that our method can not only achieves a high fall detection rate but also a low false detection. Copyright: {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/).},
	keywords = {Background subtraction, Classification (of information), Detection system, Fall detection, Features extraction, Head injuries, Human posture recognition, Indoor environment, Posture recognition, Serious injuries, Support vector machines, Video surveillance},
	doi = {10.3390/jimaging7030042},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104580792\&doi=10.3390\%2fjimaging7030042\&partnerID=40\&md5=cb6ec76b0560734c5d22b49662b3876b},
	author = {Iazzi, A. and Rziza, M. and Haj Thami, R.O.}
}
@conference {Ezzahout2013,
	title = {Conception and development of a video surveillance system for detecting, tracking and profile analysis of a person},
	booktitle = {2013 3rd International Symposium ISKO-Maghreb},
	year = {2013},
	note = {cited By 0},
	abstract = {Video surveillance is an important research area in the commercial sector as well. Many architecture of video surveillance systems are used increasingly in security systems and with good reason. Generally, with a video surveillance system, we are in the picture 24 hours a day, 7 days a week. Actually, with the rapid development of hardware and software video surveillance systems, it is economically and technically feasible to build video surveillance system architecture and tailored to a specific need. Our proposed architecture software helps to: detect, track a moving person in video sequence and helps to specify his profile when is entering in a special region from controlled area. Especially, this proposed system triggers an alarm when the monitored person enters in the prohibited area or when it exceeds a lapse of time in this region. This developed system can be used for tracking people in real time and can be adapted to extract frames from the video sequence at different times and it can process the detection process, track and it can analyze the profile of the moving person in each time. {\textcopyright} 2013 IEEE.},
	doi = {10.1109/ISKO-Maghreb.2013.6728128},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84894135551\&doi=10.1109\%2fISKO-Maghreb.2013.6728128\&partnerID=40\&md5=1fc4d25be3f941092d7c2cc02363befa},
	author = {Ezzahout, A. and Haj Thami, R.O.}
}
@conference {Romadi2013,
	title = {Detection and recognition of road signs},
	booktitle = {2013 3rd International Symposium ISKO-Maghreb},
	year = {2013},
	note = {cited By 0},
	abstract = {In this paper, w e present a n application for t h e detection and recognition of Moroccan road signs together with their positions in video sequences taken by a mobile scanning vehicle in rural areas. Our approach is based on processing t h e operations of pictures: morphology, segmentation, edge detection and object recognition. The application runs on an algorithm which consists of five main steps: input (pre-recording video stream), processing of video keyframes, edge detection, detection of geometric shapes known panels, recognizing the meaning and the position of the panel detected. {\textcopyright} 2013 IEEE.},
	doi = {10.1109/ISKO-Maghreb.2013.6728127},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84894208510\&doi=10.1109\%2fISKO-Maghreb.2013.6728127\&partnerID=40\&md5=1c3a22fd74b253e4957628821128d02c},
	author = {Romadi, M. and Haj Thami, R.O. and Chiheb, R. and Romadi, R.}
}
