@article {Iazzi2016156,
	title = {A new method for fall detection of elderly based on human shape and motion variation},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {10073 LNCS},
	year = {2016},
	note = {cited By 0},
	pages = {156-167},
	abstract = {Fall detection for elderly and patient has been an active research topic due to the great demand for products and technology of fall detection in the healthcare industry. Computer vision provides a promising solution to analyze personal behavior and detect certain unusual events such as falls. In this paper, we present a new method for fall detection based on the variation of shape and motion. First, we use the CodeBook method to extract the person silhouette from the video. Then, information of rectangle, ellipse and histogram projection are used to provide features to analyze the person shape. In addition, we represent the person shape by three blocks extracted from rectangle. Then, we use optical flow to analyze the person motion within each blocks. Finally, falls are detected from normal activities using thresholding-based method. All experiments show that our fall detection system achieves very good performances in accuracy and error rate. {\textcopyright} Springer International Publishing AG 2016.},
	doi = {10.1007/978-3-319-50832-0_16},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85007364688\&doi=10.1007\%2f978-3-319-50832-0_16\&partnerID=40\&md5=4ed27ef63754e531149224bda59c8227},
	author = {Iazzi, A.a and Rziza, M.a and Thami, R.O.H.a b and Aboutajdine, D.a}
}
@conference {Iazzi2016,
	title = {A novel approach to improve background subtraction method for fall detection system},
	booktitle = {Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA},
	volume = {2016-July},
	year = {2016},
	note = {cited By 0},
	abstract = {The fall detection system composed in general from three models, the first is a detection moving object, the second is tracking moving object and the third is recognition person{\textquoteright}s behavior. In this paper, we will deal out the first module which is the main unit in the fall detection system, because it is where we detect the person in the image. There are a lot of approaches in this way, but their results are not perfect due to the change of the background model. Many works propose to improve those results, but these approaches are not satisfied in some states. In this way, we propose a novel approach by using the optical flow which can improve The result to extract the foreground human body without errors. {\textcopyright} 2015 IEEE.},
	doi = {10.1109/AICCSA.2015.7507220},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84980390523\&doi=10.1109\%2fAICCSA.2015.7507220\&partnerID=40\&md5=8a677cb3da204800552f0e9ce8de7158},
	author = {Iazzi, A.a and Thami, R.O.H.b and Rziza, M.a}
}
