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