【摘要】 行人检测已经成为社会各领域里的热门研究课题之一。卷积神经网络CNNs（Convolutional neural networks）良好的学习能力使其学习得到的目标特征更自然,更有利于区分不同目标。但传统的卷积神经网络模型需要对整体目标进行处理,同时要求所有训练样本预先正确标注,这些阻碍了卷积神经网络模型的发展。提出一种基于卷积神经网络的隐式训练模型,该模型通过结合多部件检测模块降低计算复杂度,并采用隐式学习方法从未标注的样本中学习目标的分类规则。还提出一种两段式学习方案来逐步叠加网络的规模。在公共的静态行人检测库INRIA^[1]上的试验评测中,所提模型获得98%的检测准确率和95%的平均准确率。更多还原

【Abstract】 Pedestrian detection has become one of the hot research topics in various social fields. Convolutional neural networks have excellent learning ability. The characteristics of targets learned by these networks are more natural and more conducive to distinguishing different targets. However,traditional convolutional neural network models have to process entire target. Meanwhile,all the training samples need to be pre-labelled correctly,these hamper the development of convolutional neural network models. In this paper,we propose a convolutional neural network-based latent training model. The model reduces the computation complexity by integrating multiple part detection modules and learns the targets classification rules from unlabelled samples by adopting a latent training method. In the paper we also propose a two-stage learning scheme to overlay the size of the network step by step. Evaluation of the tests on public static pedestrian detection dataset,INRIA Person Dataset[1],demonstrates that our model achieves 98% of detection accuracy and 95% of average precision.更多还原