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

Pedestrian detection based on multi-convolutional features by feature maps pruning 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. Multimedia Tools and Applications Springer Journals

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

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Springer US
Copyright © 2017 by Springer Science+Business Media New York
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
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