Earticle

초록

영어

One new pedestrian detection method integrating static high-level features and movement features based on convolution nerve network is proposed in this paper. During the phase of unsupervised deep learning of pedestrian features, the hierarchical static features of pedestrians are extracted from the low to the high with convolution nerve network; the pedestrian movement features are obtained through mean value approach of rectangular block pixel difference. During the logic regression recognition phase, static features and movement features are integrated. The results show that the pedestrian detection algorithm of convolution nerve network integrating movement features greatly improve the pedestrian detection performance under complicated background.

목차

Abstract
 1. Introduction
 2. Deep Learning of Pedestrian Characteristics
  2.1. Convolution Nerve Network
  2.2. Non-Linear Transformation and Pooling
  2.3. Movement Feature Extraction
  2.4. Supervised Training in Combination with Movement Features
 3. Experiment Result and Analysis
  3.1. Experiment Data and Environment
  3.2. Evaluation Standard
  3.3. Contrast of Detection Precision
 4. Conclusions
 Reference

키워드

저자

• Jiang Yingjun [ School of Information Science and Engineering, Central South University ]
• Wang Jianxin [ School of Information Science and Engineering, Central South University ]
• Guo Kehua [ School of Information Science and Engineering, Central South University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Smart Home

• 간기

  격월간

• pISSN

  1975-4094

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605