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 contribution||The effect of pixel-level fusion on object tracking in multi-sensor surveillance video|
|Title of host publication||IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'07), Minneapolis, USA|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Pages||1 - 7|
|Number of pages||7|
|Publication status||Published - Jun 2007|
|Event||IEEE Conference on Computer Vision and Pattern Recognition - Minneapolis, United States|
Duration: 1 Jun 2007 → …
|Conference||IEEE Conference on Computer Vision and Pattern Recognition|
|Period||1/06/07 → …|
Bibliographical noteRose 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
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