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Authors: Diana Atef George 1 ; Omar M. Shehata 1 ; Hossam E. Abd El Munim 2 and Sherif Hammad 1

Affiliations: 1 Mechatronics Engineering Department, Faculty of Engineering, Ain Shams University, Cairo, Egypt ; 2 Computer and Systems Engineering Department, Faculty of Engineering, Ain Shams University, Cairo, Egypt

Keyword(s): Fine-grained Car Recognition, CNN, Tracking, Detecting.

Abstract: Fine-grained car recognition requires extracting discriminating features and certain car parts which can be used to distinguish between similar cars. This paper represents a full system for car make, model and year recognition in videos. We followed a multi-step approach for automatically detecting, tracking and recognizing them using deep Convolutional Neural Network (CNN). We also focused on the recognition stage where we managed to compare 4 state-of-the art Convolution Neural Networks and adapted them for extracting those features. Moreover, we modified the InceptionResnetv2 network and our results show our success as we managed to elevate the Top 1 accuracy to 0.8617 and Top 5 accuracy to 0.9751.

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Paper citation in several formats:
George, D.; Shehata, O.; El Munim, H. and Hammad, S. (2021). Video-based Car Make, Model and Year Recognition. In Proceedings of the 2nd International Conference on Robotics, Computer Vision and Intelligent Systems - ROBOVIS; ISBN 978-989-758-537-1, SciTePress, pages 86-91. DOI: 10.5220/0010649900003061

@conference{robovis21,
author={Diana Atef George. and Omar M. Shehata. and Hossam E. Abd {El Munim}. and Sherif Hammad.},
title={Video-based Car Make, Model and Year Recognition},
booktitle={Proceedings of the 2nd International Conference on Robotics, Computer Vision and Intelligent Systems - ROBOVIS},
year={2021},
pages={86-91},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010649900003061},
isbn={978-989-758-537-1},
}

TY - CONF

JO - Proceedings of the 2nd International Conference on Robotics, Computer Vision and Intelligent Systems - ROBOVIS
TI - Video-based Car Make, Model and Year Recognition
SN - 978-989-758-537-1
AU - George, D.
AU - Shehata, O.
AU - El Munim, H.
AU - Hammad, S.
PY - 2021
SP - 86
EP - 91
DO - 10.5220/0010649900003061
PB - SciTePress

- Science and Technology Publications, Lda.
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