论文标题
用于车辆制造/模型分类的数据增强和聚类
Data Augmentation and Clustering for Vehicle Make/Model Classification
论文作者
论文摘要
车辆形状信息在智能交通系统（ITS）中非常重要。在本文中，我们提出了一种利用不同年份释放的车辆的培训数据集，并以不同的观点捕获。还提出了聚类增强制造/模型分类的功效。这两个步骤都会改善分类结果和更大的鲁棒性。基于Resnet架构的更深卷积神经网络已设计用于训练车辆制造/模型分类。培训数据的不平等类分布产生先验概率。通过消除偏差和分类层中质心的严格归一化获得的消除，可改善分类结果。开发的应用程序已用于根据制造/型号和颜色分类手动测试视频数据上的车辆重新识别。这项工作是根据赠款部分资助的。
Vehicle shape information is very important in Intelligent Traffic Systems (ITS). In this paper we present a way to exploit a training data set of vehicles released in different years and captured under different perspectives. Also the efficacy of clustering to enhance the make/model classification is presented. Both steps led to improved classification results and a greater robustness. Deeper convolutional neural network based on ResNet architecture has been designed for the training of the vehicle make/model classification. The unequal class distribution of training data produces an a priori probability. Its elimination, obtained by removing of the bias and through hard normalization of the centroids in the classification layer, improves the classification results. A developed application has been used to test the vehicle re-identification on video data manually based on make/model and color classification. This work was partially funded under the grant.
