The effect of pixel-level fusion on object tracking in multi-sensor surveillance video

N Cvejic, SG Nikolov, HD Knowles, AT Loza, AM Achim, DR Bull, CN Canagarajah

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

34 Citations (Scopus)
311 Downloads (Pure)

Abstract

This paper investigates the impact of pixel-level fusion of videos from visible (VIZ) and infrared (IR) surveillance cameras on object tracking performance, as compared to tracking in single modality videos. Tracking has been accomplished by means of a particle filter which fuses a colour cue and the structural similarity measure (SSIM). The highest tracking accuracy has been obtained in IR sequences, whereas the VIZ video showed the worst tracking performance due to higher levels of clutter. However, metrics for fusion assessment clearly point towards the supremacy of the multiresolutional methods, especially Dual Tree-Complex Wavelet Transform method. Thus, a new, tracking-oriented metric is needed that is able to accurately assess how fusion affects the performance of the tracker
Translated title of the contributionThe effect of pixel-level fusion on object tracking in multi-sensor surveillance video
Original languageEnglish
Title of host publicationIEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'07), Minneapolis, USA
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1 - 7
Number of pages7
ISBN (Print)1424411807
DOIs
Publication statusPublished - Jun 2007
EventIEEE Conference on Computer Vision and Pattern Recognition - Minneapolis, United States
Duration: 1 Jun 2007 → …

Conference

ConferenceIEEE Conference on Computer Vision and Pattern Recognition
CountryUnited States
CityMinneapolis
Period1/06/07 → …

Bibliographical note

Rose publication type: Conference contribution

Sponsorship: This work has been funded by the UK Data and Information Fusion Defence Technology Centre (DIF DTC) AMDF and Tracking Cluster projects. We would like to thank the
Eden Project for allowing us to record the Eden Project Multi-Sensor Data Set (of which Eden 2.1 and 4.1 are part of) and QinetiQ, UK, for providing the QQ data set

Terms of use: Copyright © 2007 IEEE. Reprinted from IEEE Conference on Computer Vision and Pattern Recognition, 2007 (CVPR '07).

This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Bristol's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org.

By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

Fingerprint Dive into the research topics of 'The effect of pixel-level fusion on object tracking in multi-sensor surveillance video'. Together they form a unique fingerprint.

Cite this