Abstract
Most of researchers use the vehicle re-identification based on classification. This always requires an update with the new vehicle models in the market. In this paper, two types of vehicle re-identification will be presented. First, the standard method, which needs an image from the search vehicle. It produces a feature vector, which will be applied by the re-identification of the search vehicle. VRIC and VehicleID data set are suitable for training this module. It will be explained in detail how to improve the performance of this method using a trained network, which is designed for the classification. The second method takes as input a representative image of the search vehicle with similar make/model, released year and colour. It is very useful when an image from the search vehicle is not available. It produces as output a shape and a colour features. This could be used by the matching across a database to re-identify vehicles, which look similar to the search vehicle. To get a robust module for the re-identification, a fine-grained classification has been trained, which its class consists of four elements: the make of a vehicle refers to the vehicle’s manufacturer, e.g. Mercedes-Benz, the model of a vehicle refers to type of model within that manufacturer’s portfolio, e.g. C Class, the year refers to the iteration of the model, which may receive progressive alterations and upgrades by its manufacturer and the perspective of the vehicle. Thus, all four elements describe the vehicle at increasing degree of specificity. The aim of the vehicle shape classification is to classify the combination of these four elements. The colour classification has been separately trained. After the training, the classification layer will not be used. By both methods, even data of vehicles by some makes/models/released years/perspectives or by some colours are not available, it will be possible to re-identify each vehicle. The results of vehicle re-identification will be shown. Using a developed tool, the re-identification of vehicles on video images and on controlled data set using a search image will be demonstrated. The results of a proposed mix-mode, which is the combination of shape matching and colour classification, will be presented. This work was partially funded under the grant.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Nafzi, M., Brauckmann, M., Glasmachers, T.: Vehicle shape and color classification using convolutional neural network. CoRR, abs/1905.08612, March 2019. http://arxiv.org/abs/1905.08612
Kanaci, A., Zhu, X., Gong, S.: Vehicle re-identification in context. CoRR, abs/1809.09409, October 2018. http://arxiv.org/abs/1809.09409
Shen, Y., Xiao, T., Li, H., Yi, S., Wang, X.: Learning deep neural networks for vehicle re-ID with visual-spatio-temporal path proposals. CoRR, abs/1708.03918, August 2017. http://arxiv.org/abs/1708.03918
Wang, Z., Tang, L., Liu, X., Yao, Z., Yi, S., Shao, J., Yan, J., Wang, S., Li, H., Wang, X.: Orientation invariant feature embedding and spatial temporal regularization for vehicle re-identification. In: 2017 IEEE International Conference on Computer Vision (ICCV) (2017). https://doi.org/10.1109/ICCV.2017.49
Liu, H., Tian, Y., Wang, Y., Pang, L., Huang, T.: Deep relative distance learning: tell the difference between similar vehicles. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, pp. 2167–2175 (2016). http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7780607&isnumber=7780329
Dehghan, A., Masood, S.Z., Shu, G., Ortiz, E.G.: View independent vehicle make, model and color recognition using convolutional neural network. CoRR, abs/1702.01721 (2017). http://arxiv.org/abs/1702.01721
Boyle, J., Ferryman, J.: Vehicle subtype, make and model classification from side profile video. In: 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6, August 2015. https://doi.org/10.1109/AVSS.2015.7301783
Krause, J., Stark, M., Deng, J., Fei-Fei, L.: 3D object representations for fine-grained categorization. In: 2013 IEEE International Conference on Computer Vision Workshops, pp. 554–561, December 2013. https://doi.org/10.1109/ICCVW.2013.77
Petrovic, V., Cootes, T.F.: Analysis of features for rigid structure vehicle type recognition. In: Proceedings of the British Machine Vision Conference, United Kingdom, vol. 2. BMVA (2004)
Sochor, J., Herout, A., Havel, J.: Boxcars: 3D boxes as CNN input for improved fine-grained vehicle recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3006–3015 (2016)
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Nafzi, M., Brauckmann, M., Glasmachers, T. (2021). Methods of the Vehicle Re-identification. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1250. Springer, Cham. https://doi.org/10.1007/978-3-030-55180-3_38
Download citation
DOI: https://doi.org/10.1007/978-3-030-55180-3_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-55179-7
Online ISBN: 978-3-030-55180-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)