Vehicle Re-identification in Smart City Transportation using Hybrid Surveillance Systems

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


Existing traffic infrastructure management to handle traffic congestion in major cities is aging and is not effective for traffic monitoring. With an increasing demand for developing cities as smart cities, advanced Intelligent transportation systems (ITS) are in need to improve the safety and traffic movements in the cities. As an application of ITS, vehicle re-identification has gained a wide interest in the field of robotics and computer vision. Currently, these tasks are performed from the data acquired by either of the standalone surveillance systems such as CCTV or UAV. Such data acquired for re-identification poses several challenges namely viewpoints, scale, illumination change, occlusion, etc. To address these, a hybrid surveillance system approach is proposed whereby an algorithm is developed for vehicle re-identification. The re-identification algorithm is tested on a dataset containing 33 identical vehicles observed across 20 CCTV cameras and a UAV. Re-identification of vehicles is performed by estimating a transformation that maps a vehicle observed in one modality to another. The performance of vehicle re-identification is compared for a CNN network trained with the vehicle identities with and without the application of homography.

Original languageEnglish
Title of host publicationTENCON 2021 - 2021 IEEE Region 10 Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781665495325
Publication statusPublished - 2021
Event2021 IEEE Region 10 Conference, TENCON 2021 - Auckland, New Zealand
Duration: 07-12-202110-12-2021

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450


Conference2021 IEEE Region 10 Conference, TENCON 2021
Country/TerritoryNew Zealand

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering


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