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Vehicle Make and Model Recognition for CCTV Camera Footage.pdf (619.99 kB)

Vehicle make and model recognition in CCTV footage

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journal contribution
posted on 2017-03-15, 16:20 authored by Sara SaraviSara Saravi, Eran Edirisinghe
This paper presents a novel approach to Vehicle Make & Model Recognition in CCTV video footage. CPD (coherent Point Drift) is used to effectively remove skew of vehicles detected as CCTV cameras are not specifically configured for the VMMR (Vehicle Make and Model Recognition) task and may capture vehicles at different approaching angles. Also a novel ROI (Region Of Interest) segmentation is proposed. A LESH (Local Energy Shape Histogram) feature based approach is used for vehicle make and model recognition with the novelty that temporal processing is used to improve reliability. A number of further algorithms are used to maximize the reliability of the fnal outcome. Experimental results are provided to prove that the proposed system demonstrates accuracy over 95% when tested in real CCTV footage with no prior camera calibration.

History

School

  • Science

Department

  • Computer Science

Published in

2013 18th International Conference on Digital Signal Processing, DSP 2013

Citation

SARAVI, S. and EDIRISINGHE, E., 2013. Vehicle make and model recognition in CCTV footage. IN: Proceedings of 2013 18th International Conference on Digital Signal Processing (DSP 2013), Santorini, Greece, 1-3 July 2013, DOI: 10.1109/ICDSP.2013.6622720.

Publisher

© IEEE

Version

  • AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2013

Notes

© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

ISBN

9781467358071;9781467358064

ISSN

1546-1874

eISSN

2165-3577

Language

  • en