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Data Augmentation and Clustering for Vehicle Make/Model Classification

2020-09-14 18:24:31
Mohamed Nafzi, Michael Brauckmann, Tobias Glasmachers
arXiv_CV
       
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2009.06679

PDF

https://arxiv.org/pdf/2009.06679.pdf

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