Scalable object detection pipeline for traffic cameras: Application to Tfl JamCams
File(s)
Accepted version
Author(s)
Gan, Huan Min
Fernando, Senaka
Molina-Solana, Miguel
Type
Journal Article
Abstract
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.
Date Issued
2021-11-15
Date Acceptance
2021-05-02
Citation
Expert Systems with Applications, 2021, 182, pp.1-15
ISSN
0957-4174
Publisher
Elsevier BV
Start Page
1
End Page
15
Journal / Book Title
Expert Systems with Applications
Volume
182
Copyright Statement
© 2021 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/
Sponsor
European Commission
European Commission Directorate-General for Research and Innovation
Identifier
https://www.sciencedirect.com/science/article/pii/S0957417421005959?via%3Dihub
Grant Number
GA 743623
Subjects
01 Mathematical Sciences
08 Information and Computing Sciences
09 Engineering
Artificial Intelligence & Image Processing
Publication Status
Published
Article Number
115154
Date Publish Online
2021-05-07