Related papers: Data Augmentation and Clustering for Vehicle Make/Model Classification

Data Augmentation and Clustering for Vehicle Make/Model Classification

URL: http://arxiv.org/abs/2009.06679v1
Date: Mon, 14 Sep 2020 18:24:31 GMT
Title: Data Augmentation and Clustering for Vehicle Make/Model Classification
Authors: Mohamed Nafzi, Michael Brauckmann, Tobias Glasmachers
Abstract summary: 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. Deeper convolutional neural network based on ResNet architecture has been designed for the training of the vehicle make/model classification.
Score: 0.0
License: http://creativecommons.org/publicdomain/zero/1.0/
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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