WiSECam: A CSI-Based Deep Learning Motion Detection for Wireless Cameras

2021 ◽  
Vol 46 (12) ◽  
pp. 2184-2190
Author(s):  
DinhNguyen Dao ◽  
Muhammad Salman ◽  
YoungTae Noh
Keyword(s):  
Deep Learning ◽  
Motion Detection

Radar fall motion detection using deep learning

2016 ◽  
Author(s):  
Branka Jokanovic ◽  
Moeness Amin ◽  
Fauzia Ahmad
Keyword(s):  
Deep Learning ◽  
Motion Detection

scholarly journals Wild Animal Recognition Method for Videos Using a Combination of Deep Learning and Motion Detection

2021 ◽  
Vol 9 (1) ◽  
pp. 38-45
Author(s):  
彩奈 中島 ◽  
浩之 奥 ◽  
和弘 茂木 ◽  
洋一 白石
Keyword(s):  
Deep Learning ◽  
Motion Detection ◽  
Wild Animal ◽  
Recognition Method

scholarly journals Deep Learning-Driven Gaussian Modeling and Improved Motion Detection Algorithm of the Three-Frame Difference Method

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Dingchao Zheng ◽  
Yangzhi Zhang ◽  
Zhijian Xiao
Keyword(s):  
Deep Learning ◽  
Motion Detection ◽  
Adaptive Learning ◽  
Difference Method ◽  
Gaussian Model ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Frame Difference ◽  
Gaussian Modeling ◽  
Difference Calculation

To enhance the effect of motion detection, a Gaussian modeling algorithm is proposed to fix holes and breaks caused by the conventional frame difference method. The proposed algorithm uses an improved three-frame difference method. A three-frame image sequence with one frame interval is selected for pairwise difference calculation. The logical “OR” operation is used to achieve fast motion detection and to reduce voids and fractures. The Gaussian algorithm establishes an adaptive learning model to make the size and contour of the motion detection more accurate. The motion extracted by the improved three-frame difference method and Gaussian model is logically summed to obtain the final motion foreground picture. Moreover, a moving target detection method, based on the U-Net deep learning network, is proposed to reduce the dependency of deep learning on the number of training datasets. It helps the algorithm to train models on small datasets. Next, it calculates the ratio of the number of positive and negative samples in the dataset and uses the reciprocal of the ratio as the sample weight to deal with the imbalance of positive and negative samples. Finally, a threshold is set to predict the results for obtaining the moving object detection accuracy. Experimental results show that the algorithm can suppress the generation and rupture of holes and reduce the noise. Also, it can quickly and accurately detect movement to meet the design requirements.

Granulated deep learning and Z-numbers in motion detection and object recognition

2019 ◽  
Vol 32 (21) ◽  
pp. 16533-16548 ◽  
Author(s):  
Sankar K. Pal ◽  
Debasmita Bhoumik ◽  
Debarati Bhunia Chakraborty
Keyword(s):  
Deep Learning ◽  
Object Recognition ◽  
Motion Detection

scholarly journals Falling motion detection algorithm based on deep learning

2021 ◽  
Author(s):  
Na Zhu ◽  
Guangzhe Zhao ◽  
Xiaolong Zhang ◽  
Zhexue Jin
Keyword(s):  
Deep Learning ◽  
Motion Detection ◽  
Detection Algorithm

iFitness: A Deep Learning-Based Physical Fitness Motion Detection System for Elderly People

2021 ◽  
Author(s):  
Wan-Jung Chang ◽  
Jian-Ping Su ◽  
Chia-Hao Chen ◽  
Shu-Ching Liu ◽  
Chih-Ching Chang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Physical Fitness ◽  
Elderly People ◽  
Motion Detection ◽  
Detection System ◽  
Motion Detection System

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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