@article {Iazzi2020247,
	title = {Efficient fall activity recognition by combining shape and motion features},
	journal = {Computational Visual Media},
	volume = {6},
	number = {3},
	year = {2020},
	note = {cited By 2},
	pages = {247-263},
	abstract = {This paper presents a vision-based system for recognizing when elderly adults fall. A fall is characterized by shape deformation and high motion. We represent shape variation using three features, the aspect ratio of the bounding box, the orientation of an ellipse representing the body, and the aspect ratio of the projection histogram. For motion variation, we extract several features from three blocks corresponding to the head, center of the body, and feet using optical flow. For each block, we compute the speed and the direction of motion. Each activity is represented by a feature vector constructed from variations in shape and motion features for a set of frames. A support vector machine is used to classify fall and non-fall activities. Experiments on three different datasets show the effectiveness of our proposed method. {\textcopyright} 2020, The Author(s).},
	keywords = {Activity recognition, Aspect ratio, Direction of motion, Feature vectors, Motion variation, Optical flows, Projection histograms, Shape deformation, Shape variations, Support vector machines, Vision based system},
	doi = {10.1007/s41095-020-0183-7},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090864771\&doi=10.1007\%2fs41095-020-0183-7\&partnerID=40\&md5=21fcdf876b091c6b30486427e7cd9bff},
	author = {Iazzi, A. and Rziza, M. and Thami, R.O.H.}
}
