基于卷积神经网络的固定群体中目标人物分类

doi:10.12066/j.issn.1007-2861.1912

Abstract

Abstract:

As an important branch of deep learning, convolutional neural network has been widely used in image recognition, and image classification. The convolutional architecture for fast feature embedding (Caffe) is the most popular tool in deep learning. A method of classification of individual objects in a focused group is proposed based on convolutional neural network independent of the face image set. It captures video with a camera, and obtains training images using a method of normalized cross-correlation histogram. Caffe is used to generate a training model that can realize the classification of individual objects in a focused group of people. Experimental results show that, by using a pre-training model, individual objects can be matched accurately.

Key words: classification of individual object, convolutional architecture for fast feature embedding (Caffe), focused group, convolutional neural network (CNN)

LIU Huibin, CHEN Qiang, WU Fei, ZHAO Yi. Classification of individual objects in focused group based on convolutional neural network[J]. Journal of Shanghai University（Natural Science Edition）, 2017, 23(6): 874-.

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Classification of individual objects in focused group based on convolutional neural network

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