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Feature Visualization Based Stacked Convolutional Neural Network for Human Body Detection in a Depth Image

  • Xiao Liu
  • Ling Mei
  • Dakun Yang
  • Jianhuang Lai
  • Xiaohua XieEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11257)

Abstract

Human body detection is a key technology in the fields of biometric recognition, and the detection in a depth image is rather challenging due to serious noise effects and lack of texture information. For addressing this issue, we propose the feature visualization based stacked convolutional neural network (FV-SCNN), which can be trained by a two-layer unsupervised learning. Specifically, the next CNN layer is obtained by optimizing a sparse auto-encoder (SAE) on the reconstructed visualization of the former to capture robust high-level features. Experiments on SZU Depth Pedestrian dataset verify that the proposed method can achieve favorable accuracy for body detection. The key of our method is that the CNN-based feature visualization actually pursues a data-driven processing for a depth map, and significantly alleviates the influences of noise and corruptions on body detection.

Keywords

Human detection Depth image Feature visualization Sparse auto-encoder Convolutional neural network 

Notes

Acknowledgements

This project is supported by the Natural Science Foundation of China (61573387, 61672544), Guangzhou Project (201807010070), and Fundamental Research Funds for the Central Universities (No. 161gpy41).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Sun Yat-sen UniversityGuangzhouChina
  2. 2.Guangdong Key Laboratory of Information Security TechnologyGuangzhouChina
  3. 3.Key Laboratory of Machine Intelligence and Advanced ComputingMinistry of EducationGuangzhouChina

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