In the vehicle type classification area, the necessity to improve classification performance across traffic surveillance cameras has garnered attention in research especially on high level feature extraction and classification. The backpropagation (BP) training approach of traditional deep Convolutional Neural Network (CNN) approach is time-consuming without using a Graphics Processing Unit (GPU). In this paper, we propose a semisupervised strategy for the end-to-end Principal Component Analysis Convolutional Network (PCN) in the area of vehicle type classification. Even without using a GPU, the proposed model eliminates the time-consuming training procedure of convolutional filter bank. In particular, the convolutional filters of the network are generated using unsupervised learning by Principal Component Analysis (PCA) which has tremendously reduced training cost and also reinforced the robustness of extracted features against various distortions. In order to further improve the training procedure while still preserving the discriminative characteristic of the system, only the fully-connected layer is fine-tuned in the supervised classification stage. The PCN is tested using a public BIT-Vehicle dataset which comprises 9850 surveillance-nature vehicle frontal-view images. The PCN can be easily implemented and readily compatible with many effective classifiers. Two classifiers, namely softmax classifier and Support Vector Machine (SVM) are employed in this network and their classification performances are then compared. Both classifiers take less than 100 seconds in the training process and are able to produce an average accuracy of above 88.35%, even under various inferior imaging conditions.

中文翻译：

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用于车辆类型分类的半监督 PCA 卷积网络

在车辆类型分类领域，提高交通监控摄像机分类性能的必要性引起了研究的关注，特别是在高级特征提取和分类方面。传统深度卷积神经网络 (CNN) 方法的反向传播 (BP) 训练方法在不使用图形处理单元 (GPU) 的情况下非常耗时。在本文中，我们为车辆类型分类领域的端到端主成分分析卷积网络（PCN）提出了一种半监督策略。即使不使用 GPU，所提出的模型也消除了卷积滤波器组耗时的训练过程。特别是，网络的卷积滤波器是通过主成分分析 (PCA) 使用无监督学习生成的，这极大地降低了训练成本，并增强了提取特征对各种失真的鲁棒性。为了进一步改进训练过程，同时仍保留系统的判别特性，在监督分类阶段仅对全连接层进行微调。PCN 使用公共 BIT-Vehicle 数据集进行测试，该数据集包含 9850 个监视性质的车辆正面视图图像。PCN 可以很容易地实现并且很容易与许多有效的分类器兼容。该网络采用两个分类器，即softmax分类器和支持向量机（SVM），然后比较它们的分类性能。