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MAT-CNN-SOPC: Traffic Analysis Using CNNs on FPGA

License: MIT

Motionless Analysis of Traffic Using Convolutional Neural Networks on System-on-a-programmable-chip: MAT-CNN-FPGA

Train simple CNN (using Matlab)

Use the vgg16_custom.m in /source/matlab/. Execution:

>> vgg16_custom()

Train simple CNN (using Keras in Python)

Use the train_routine.py in /source/python/. Varying parameters are :

pre_trained_model='VGG16'

Also you can change the number of training epochs inside source/python/engine/bottleneck_features.py

The base_dir and base_dir_trained_models variables must be adapted accordingly.

Accuracy Result

Face Detection Using OpenCV and Resource Monitoring

Citation

MAT-CNN-SOPC is presented at 2018 NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2018). Link: https://www.ahs-conf.org

If you are using the MAT-CNN-SOPC code then please do cite the paper as follows:

Dey, S., Kalliatakis, G., Saha, S., Singh, A. K., Ehsan, S., & McDonald-Maier, K. (2018, August). 
MAT-CNN-SOPC: Motionless Analysis of Traffic Using Convolutional Neural Networks on System-On-a-Programmable-Chip. 
In 2018 NASA/ESA Conference on Adaptive Hardware and Systems (AHS) (pp. 291-298). IEEE.

Bib:

@inproceedings{dey2018mat,
  title={MAT-CNN-SOPC: Motionless Analysis of Traffic Using Convolutional Neural Networks on System-On-a-Programmable-Chip},
  author={Dey, Somdip and Kalliatakis, Grigorios and Saha, Sangeet and Singh, Amit Kumar and Ehsan, Shoaib and McDonald-Maier, Klaus},
  booktitle={2018 NASA/ESA Conference on Adaptive Hardware and Systems (AHS)},
  pages={291--298},
  year={2018},
  organization={IEEE}
}