Research on Pedestrian Detection Based on Faster R-CNN and Hippocampal Neural Network

B. Hao, Su-Bin Park, D. Kang
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引用次数: 2

Abstract

This paper use Faster-RCNN and hippocampal neural network algorithms to research. Firstly use convolutional neural network to extract the features of the input image, and then use Region Proposal Networks to extract the standard frame. Here we can judge whether there are objects in the standard frame and know the location of the standard box, then use the Non-Maximum Suppression to select the standard box, finally perform the classification operation and regression operation. The final classification network is the hippocampal neural network. The hippocampal neural network is a spatial structure model that mimics the hippocampus of human brain.
基于更快R-CNN和海马神经网络的行人检测研究
本文采用Faster-RCNN和海马神经网络算法进行研究。首先使用卷积神经网络提取输入图像的特征,然后使用区域建议网络提取标准帧。在这里我们可以判断标准框中是否有对象,知道标准框的位置,然后使用非最大抑制来选择标准框,最后进行分类运算和回归运算。最后一个分类网络是海马体神经网络。海马体神经网络是一种模仿人脑海马体的空间结构模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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