Data Augmentation and Clustering for Vehicle Make/Model Classification

Data Augmentation and Clustering for Vehicle Make/Model Classification

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.

车辆品牌/车型分类的数据扩充和聚类

车辆形状信息在智能交通系统（ITS）中非常重要。在本文中，我们提出了一种方法，以利用不同年份发布并以不同视角捕获的车辆训练数据集。.. 还介绍了聚类以增强品牌/型号分类的功效。这两个步骤都导致改进的分类结果和更大的鲁棒性。已经设计了基于ResNet架构的更深层卷积神经网络，用于训练车辆制造商/模型分类。训练数据的不均等分布会产生先验概率。通过消除偏见并通过对归类层中的质心进行严格归一化而消除它，可以改善分类结果。基于品牌/型号和颜色分类，已开发的应用程序已用于手动测试视频数据上的车辆重新识别。这项工作的部分资金来自赠款。 （阅读更多）