COMPUTATIONALLY EFFICIENT, REAL-TIME MOTION RECOGNITION BASED ON BIO-INSPIRED VISUAL AND COGNITIVE PROCESSING

被引：0

作者：

Park, Paul K. J. ^{[1
]}

Lee, Kyoobin ^{[1
]}

Lee, Jun Haeng ^{[1
]}

Kang, Byungkon ^{[1
]}

Shin, Chang-Woo ^{[1
]}

Woo, Jooyeon ^{[1
]}

Kim, Jun-Seok ^{[1
]}

Suh, Yunjae ^{[1
]}

Kim, Sungho ^{[1
]}

Moradi, Saber ^{[1
]}

Gurel, Ogan ^{[1
]}

Ryu, Hyunsurk ^{[1
]}

机构：

[1] Samsung Elect, SAIT, Samsung Ro 130, Suwon 443803, South Korea

来源：

2015 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2015年

关键词：

motion; recognition; bio-inspired; convolutional; neural network;

D O I：

暂无

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：

We propose a novel method for identifying and classifying motions that offers significantly reduced computational cost as compared to deep convolutional neural network systems with comparable performance. Our new approach is inspired by the information processing network architecture of biological visual processing systems, whereby spatial pyramid kernel features are efficiently extracted in real-time from temporally-differentiated image data. In this paper, we describe this new method and evaluate its performance with a hand motion gesture recognition task.

引用

页码：932 / 935

页数：4

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