Pedestrian-Counting

A deep learning pipeline and an image-scraper to count pedestrians from traffic camera images on 511NY. The utilized deep learning model is the Faster R-CNN built upon the ResNet-50 architecture, pre-trained on MS-COCO dataset. Other deep learning models can easily be swapped in with the utilized GluonCV library. This work represents a proof-of-concept, and higher accuracy can be achieved through more rigorous model training and a more specialized, labeled training dataset containing more similar images with labeled pedestrians.

Published Paper DOI

Citation

• E. Deleu, S. Elez, A. Gadodia, K. Macvaugh and G. Zhao, "Using Deep Learning for Urban Pedestrian Counting," 2021 IEEE MIT Undergraduate Research Technology Conference (URTC), Cambridge, MA, USA, 2021, pp. 1-5, doi: 10.1109/URTC54388.2021.9701649.

@INPROCEEDINGS{deleu2021pedestrians,
  author={Deleu, Edward and Elez, Stefan and Gadodia, Ansh and Macvaugh, Kyra and Zhao, Grace},
  booktitle={2021 IEEE MIT Undergraduate Research Technology Conference (URTC)}, 
  title={Using Deep Learning for Urban Pedestrian Counting}, 
  year={2021},
  volume={},
  number={},
  pages={1-5},
  doi={10.1109/URTC54388.2021.9701649}
}