@article {ElKaid2022,
	title = {Top-Down System for Multi-Person 3D Absolute Pose Estimation from Monocular Videos},
	journal = {Sensors},
	volume = {22},
	number = {11},
	year = {2022},
	note = {cited By 2},
	abstract = {Two-dimensional (2D) multi-person pose estimation and three-dimensional (3D) root-relative pose estimation from a monocular RGB camera have made significant progress recently. Yet, real-world applications require depth estimations and the ability to determine the distances between people in a scene. Therefore, it is necessary to recover the 3D absolute poses of several people. However, this is still a challenge when using cameras from single points of view. Furthermore, the previously proposed systems typically required a significant amount of resources and memory. To overcome these restrictions, we herein propose a real-time framework for multi-person 3D absolute pose estimation from a monocular camera, which integrates a human detector, a 2D pose estimator, a 3D root-relative pose reconstructor, and a root depth estimator in a top-down manner. The proposed system, called Root-GAST-Net, is based on modified versions of GAST-Net and RootNet networks. The efficiency of the proposed Root-GAST-Net system is demonstrated through quantitative and qualitative evaluations on two benchmark datasets, Human3.6M and MuPoTS-3D. On all evaluated metrics, our experimental results on the MuPoTS-3D dataset outperform the current state-of-the-art by a significant margin, and can run in real-time at 15 fps on the Nvidia GeForce GTX 1080. {\textcopyright} 2022 by the authors. Licensee MDPI, Basel, Switzerland.},
	keywords = {3d multi-person pose estimation, Absolute pose, algorithm, Algorithms, Camera-centric coordinate, Cameras, Computer vision, Deep learning, Deep-learning, human, Humans, Imaging, Monocular video, Pose-estimation, procedures, Real- time, Relative pose, Three-Dimensional, Three-dimensional imaging, Topdown, Two-dimensional},
	doi = {10.3390/s22114109},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130790166\&doi=10.3390\%2fs22114109\&partnerID=40\&md5=7009c9053fd9d12efcd40ade931932f7},
	author = {El Kaid, A. and Brazey, D. and Barra, V. and Baina, K.}
}
@article {8699111820120101,
	title = {Reduced Complexity Iterative Decoding of 3D-Product Block Codes Based on Genetic Algorithms.},
	journal = {Journal of Electrical \& Computer Engineering},
	year = {2012},
	pages = {1 - 8},
	abstract = {Two iterative decoding algorithms of 3D-product block codes (3D-PBC) based on genetic algorithms (GAs) are presented. The first algorithm uses the Chase-Pyndiah SISO, and the second one uses the list-based SISO decoding algorithm (LBDA) based on order-i reprocessing. We applied these algorithms over AWGN channel to symmetric 3D-PBC constructed from BCH codes. The simulation results show that the first algorithm outperforms the Chase-Pyndiah one and is only 1.38 dB away from the Shannon capacity limit at BER of 10-5 for BCH (31, 21, 5){\textthreesuperior} and 1.4 dB for BCH (16, 11, 4){\textthreesuperior}. The simulations of the LBDA-based GA on the BCH (16, 11, 4){\textthreesuperior} show that its performances outperform the first algorithm and is about 1.33 dB from the Shannon limit. Furthermore, these algorithms can be applied to any arbitrary 3D binary product block codes, without the need of a hard-in hardout decoder. We show also that the two proposed decoders are less complex than both Chase-Pyndiah algorithm for codes with large corr},
	keywords = {Computational complexity, Computer simulation, Genetic algorithms, Iterative decoding, Performance evaluation, Three-dimensional imaging},
	issn = {20900147},
	url = {http://search.ebscohost.com/login.aspx?direct=true\&db=iih\&AN=86991118\&site=ehost-live},
	author = {Ahmadi, Abdeslam and Bouanani, Faissal El and Ben-Azza, Hussain and Benghabrit, Youssef}
}
