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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Acknowledgment
– Victoria: Funded by the European Commission (H2020), Grant Agreement number 740754 and is for Video analysis for Investigation of Criminal and Terrorist Activities.
– Florida: Funded by the German Ministry of Education and Research (BMBF).
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Nafzi, M., Brauckmann, M., Glasmachers, T. (2020). Data Augmentation and Clustering for Vehicle Make/Model Classification. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2020. Advances in Intelligent Systems and Computing, vol 1228. Springer, Cham. https://doi.org/10.1007/978-3-030-52249-0_24
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