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Graininess-Aware Deep Feature Learning for Pedestrian Detection

  • Chunze Lin
  • Jiwen Lu
  • Gang Wang
  • Jie Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11213)

Abstract

In this paper, we propose a graininess-aware deep feature learning method for pedestrian detection. Unlike most existing pedestrian detection methods which only consider low resolution feature maps, we incorporate fine-grained information into convolutional features to make them more discriminative for human body parts. Specifically, we propose a pedestrian attention mechanism which efficiently identifies pedestrian regions. Our method encodes fine-grained attention masks into convolutional feature maps, which significantly suppresses background interference and highlights pedestrians. Hence, our graininess-aware features become more focused on pedestrians, in particular those of small size and with occlusion. We further introduce a zoom-in-zoom-out module, which enhances the features by incorporating local details and context information. We integrate these two modules into a deep neural network, forming an end-to-end trainable pedestrian detector. Comprehensive experimental results on four challenging pedestrian benchmarks demonstrate the effectiveness of the proposed approach.

Keywords

Pedestrian detection Attention Deep learning Graininess 

Notes

Acknowledgment

This work was supported in part by the National Key Research and Development Program of China under Grant 2017YFA0700802, in part by the National Natural Science Foundation of China under Grant 61822603, Grant U1713214, Grant 61672306, Grant 61572271, and in part by the Shenzhen Fundamental Research Fund (Subject Arrangement) under Grant JCYJ20170412170602564.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Tsinghua UniversityBeijingChina
  2. 2.Alibaba AI LabsHangzhouChina

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