If you find this code useful in your research, please consider citing:

@article{icaroICIP2019,
    title={A Two-Stream Siamese Neural Network for Vehicle Re-Identification by Using Non-Overlapping Cameras},
    author={de Oliveira, Icaro O and Fonseca, Keiko VO and Minetto, Rodrigo},
    journal={IEEE International Conference on Image Processing (ICIP)},
    year={2019}
}

• Ícaro Oliveira de Oliveira
• Keiko Veronica Ono Fonseca
• Rodrigo Minetto

This research was conducted while the authors were at the Universidade Tecnológica Federal do Paraná (UTFPR). We describe in this paper a novel Two-Stream Siamese Neural Network for vehicle re-identification.

The proposed network is fed simultaneously with small coarse patches of the vehicle shape's, with 96 x 96 pixels, in one stream, and fine features extracted from license plate patches, easily readable by humans, with 96 x 48 pixels, in the other one.

Then, we combined the strengths of both streams by merging the siamese distance descriptors with a sequence of fully connected layers, as an attempt to tackle a major problem in the field, false alarms caused by a huge number of car design and models with nearly the same appearance or by similar license plate strings.

In our experiments, with 2 hours of videos containing 2982 vehicles, extracted from two low-cost cameras in the same roadway, 546 ft away, we achieved a F-measure and accuracy of 92.6% and 98.7%, respectively. We show that the proposed network outperforms other One-Stream architectures, even if they use higher resolution image features.

pip install keras tensorflow scikit-learn futures

config.py

python generate_dataset.py

{'trn':[[img1_plate, img1_car, img2_plate, img2_car, label], ...], 'tst':[[img1_plate, img1_car, img2_plate, img2_car, label], ...]}

python siamese.py plate

python siamese.py car

python siamese_two_stream.py

python siamese_test.py siamese_original_two_stream.h5 dataset1_1.json