Multimedia Tools and Applications

, Volume 76, Issue 23, pp 25079–25089 | Cite as

Pedestrian detection based on multi-convolutional features by feature maps pruning

  • Ting Rui
  • Junhua Zou
  • You Zhou
  • Husheng Fang
  • Qiyu Gao


Convolutional neural network (CNN) has developed such a large network size in last few years, so reducing the storage requirement without hurting its accuracy becomes necessary. In this paper, in order to reduce the number of high dimensional feature maps in shallow layers, we propose a feature map selection method, which cuts the feature map number by correlation coefficient between kernels and finishes detection by HOG+SVM method. Firstly, we extract feature maps of shallow layers from trained CNN. Then, we merge strongly relevant feature maps and choose all maps among weakly relevant feature maps by analyzing correlation coefficient of kernels. Finally, we extract HOG features of the chosen feature maps and use SVM to complete the training and classification. The experimental results show that the proposed method can effectively prune high dimensional feature maps and stabilize or even advance the performance in pedestrian detection.


Pedestrian detection Convolutional neural network Feature maps pruning Histogram of oriented gradient 



This work was supported in part by National Natural Science Foundation of China: 61472444, 61472392.


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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.PLA University of Science and TechnologyQinhuai District NanjingChina
  2. 2.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjing ShiChina
  3. 3.College of Machanical EngineeringJiangsu Institute of CommerceQinhuai District NanjingChina

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