<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aziz Khelalef</style></author><author><style face="normal" font="default" size="100%">Fakhreddine Ababsa</style></author><author><style face="normal" font="default" size="100%">Nabil Benoudjit</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An efficient human activity recognition technique based on deep learning</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition and Image Analysis</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">29</style></volume><pages><style face="normal" font="default" size="100%">702-715</style></pages><isbn><style face="normal" font="default" size="100%">1555-6212</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">4</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aziz Khelalef</style></author><author><style face="normal" font="default" size="100%">Fakhreddine Ababsa</style></author><author><style face="normal" font="default" size="100%">Nabil Benoudjit</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Sample Human Activity Recognition Technique Using DCT</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Advanced Concepts for Intelligent Vision Systems. ACIVS 2016</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	In this paper, we present a simple new human activity recognition method using discrete cosine transform (DCT). The scheme uses the DCT coefficients extracted from silhouettes as descriptors (features) and performs frame-by-frame recognition, which make it simple and suitable for real time applications. We carried out several tests using radial basis neural network (RBF) for classification, a comparative study against stat-of-the-art methods shows that our technique is faster, simple and gives higher accuracy performance comparing to discrete transform based techniques and other methods proposed in literature.
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