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arXiv · 2009.06679

Data Augmentation and Clustering for Vehicle Make/Model Classification

Mohamed Nafzi·Michael Brauckmann·Tobias Glasmachers
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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.

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Mohamed Nafzi, Michael Brauckmann, Tobias Glasmachers. 2020-09-14. Data Augmentation and Clustering for Vehicle Make/Model Classification. https://doi.org/10.1007/978-3-030-52249-0_24

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