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A Method of Detecting Human Head by Eliminating Redundancy in Dataset

  • Chao Le
  • Huimin Ma
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 875)

Abstract

The method of constructing an image dataset by sampling images from videos with a short interval keeps the information in the video but also brings redundancy and increases the training costs significantly. In this paper, we propose a method to detect human heads with less training cost and higher performance, including: (1) A filtering standard to screen out the useless image in video-based image dataset with almost the same average precision. (2) An effective head detection model with the fusion of shoulder context. We evaluate our method on a human head dataset – HollywoodHeads and achieve reasonably good performance. This result shows that our method is very useful in human head detection task.

Keywords

Convolutional neural network Dataset filtering Head detection 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Electronic EngineeringTsinghua UniversityBeijingChina

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