Deep Neural Networks vs Bag of Features for Hand Gesture Recognition

被引：2

作者：

Mirsu, Radu ^{[1
]}

Simion, Georgiana ^{[1
]}

Caleanu, Catlin Daniel ^{[1
]}

Ursulescu, Oana ^{[1
]}

Calimanu, Ioana Pop ^{[1
]}

机构：

[1] Politehn Univ Timisoara, Fac Elect Telecommun & Informat Technol, Timisoara, Romania

来源：

2019 42ND INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS AND SIGNAL PROCESSING (TSP) | 2019年

关键词：

bag of feature; deep neural netorks; hand gestures; SURF;

D O I：

10.1109/tsp.2019.8768812

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Deep Neural Networks and their associated learning paradigm, Deep Learning, represent one of the hottest approaches used in image understanding and recognition. As their performances depends quasi-linearly on the amount of available data, the typical case studies in the literature assume the availability of huge datasets. This paper proposes to analyze several deep neural networks (trained from the scratch or pretrained), test their efficiency in the problem of hand gesture recognition, and compare the results to a state-of-the-art classical method, the bag of features, for the case of small databases.

引用

页码：369 / 372

页数：4

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