<?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><urls><web-urls><url><style face="normal" font="default" size="100%">https://link.springer.com/article/10.1134/S1054661819040084</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">29</style></volume><pages><style face="normal" font="default" size="100%">702-715</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this paper, we present a new deep learning-based human activity recognition technique. First, we track and extract human body from each frame of the video stream. Next, we abstract human silhouettes and use them to create binary space-time maps (BSTMs) which summarize human activity within a defined time interval. Finally, we use convolutional neural network (CNN) to extract features from BSTMs and classify the activities. To evaluate our approach, we carried out several tests using three public datasets: Weizmann, Keck Gesture and KTH Database. Experimental results show that our technique outperforms conventional state-of-the-art methods in term of recognition accuracy and provides comparable performance against recent deep learning techniques. It’s simple to implement, requires less computing power, and can be used for multi-subject activity recognition.</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue></record></records></xml>