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Performance Improvement of Object Recognition System in Broadcast Media Using Hierarchical CNN

계층적 CNN을 이용한 방송 매체 내의 객체 인식 시스템 성능향상 방안

  • 권명규 (호서대학교 벤처대학원 융합공학과) ;
  • 양효식 (삼일회계법인)
  • Received : 2017.01.31
  • Accepted : 2017.03.20
  • Published : 2017.03.28

Abstract

This paper is a smartphone object recognition system using hierarchical convolutional neural network. The overall configuration is a method of communicating object information to the smartphone by matching the collected data by connecting the smartphone and the server and recognizing the object to the convergence neural network in the server. It is also compared to a hierarchical convolutional neural network and a fractional convolutional neural network. Hierarchical convolutional neural networks have 88% accuracy, fractional convolutional neural networks have 73% accuracy and 15%p performance improvement. Based on this, it shows possibility of expansion of T-Commerce market connected with smartphone and broadcasting media.

Keywords

Convolutional Neural Network;T-Commerce;Deep Learning;Object Recognition;Pooling

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