基于分层深度学习的鲁棒行人分类

Robust Pedestrian Classification Based on Hierarchical Deep Learning

针对行人分类中常见的光照条件、形体变化以及遮挡等多种因素,对特征提取过程造成了很大的阻碍。本文提出一种基于稀疏编码的分层特征提取方法。该方法采用前向预测函数训练最优的稀疏编码,在深度卷积网络模型的框架下以卷积预测稀疏分解算法(CPSD)分别对两层模型进行无监督学习,将两层的特征融合起来,最后采用支持向量机算法实现行人分类。实验结果表明,该文特征学习方法对行人分类的有效性,对比同类方法性能有明显提升。

In pedestrian classification, there are many factors, such as light changes, posture changes and occlusion problems etc, which brings many difficulties for feature extraction process. A hierarchical feature method is put forward based on sparse coding. The method trains optimal sparse coding with forward prediction function, and then learns the two levels networks one by one in unsupervised manner with Convolution Predictive Sparse Decomposition algorithm （CPSD） under framework of the deep convolution network model. Then we make the feature fusion. Finally, we implement classification with SVM algorithm. Experimental results demonstrate the effectiveness of our method for pedestrian classification, which has significant performance improvement compared with similar methods.