In this section
Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.
@article{Gan:2021:10.1016/j.eswa.2021.115154,
author = {Gan, HM and Fernando, S and Molina-Solana, M},
doi = {10.1016/j.eswa.2021.115154},
journal = {Expert Systems with Applications},
pages = {1--15},
title = {Scalable object detection pipeline for traffic cameras: Application to Tfl JamCams},
url = {http://dx.doi.org/10.1016/j.eswa.2021.115154},
volume = {182},
year = {2021}
}
TY - JOUR
AB - With CCTV systems being installed in the transport infrastructures of many cities, there is an abundance of data to be extracted from the footage. This paper explores the application of the YOLOv3 object detection algorithm trained on the COCO dataset to the Transport for London’s (TfL) JamCam feed. The result, open-sourced and publicly available, is a series of easy to deploy Docker pipelines to create, store and serve (through a REST API) data on identified objects on that feed. The pipelines can be deployed to any Linux machine with an NVIDIA GPU to support accelerated computation. We studied how different confidence thresholds affect detections of relevant objects (cars, trucks and pedestrians) in London JamCam scenes. By running the system continuously for 3 weeks, we built a dataset of more than 2200 detection datapoints for each camera (6 datapoints an hour). We further visualised the detections on an animated geospatial map, showcasing their effectiveness in identifying traffic patterns typical of an urban city like London, portraying the variation on different object population levels throughout the day.
AU - Gan,HM
AU - Fernando,S
AU - Molina-Solana,M
DO - 10.1016/j.eswa.2021.115154
EP - 15
PY - 2021///
SN - 0957-4174
SP - 1
TI - Scalable object detection pipeline for traffic cameras: Application to Tfl JamCams
T2 - Expert Systems with Applications
UR - http://dx.doi.org/10.1016/j.eswa.2021.115154
UR - https://www.sciencedirect.com/science/article/pii/S0957417421005959?via%3Dihub
UR - http://hdl.handle.net/10044/1/88829
VL - 182
ER -
Data Science Institute
William Penney Laboratory
Imperial College London
South Kensington Campus
London SW7 2AZ
United Kingdom
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