Abstract
Robust moving video object tracking under illumination variations, occlusion, object scale and appearance changes is a challenging problem. Bayesian filtering in particular particle filtering is conventionally used for nonlinear and non-Gaussian object state estimation problems because of its high performance. In this paper we extend the color based variable rate particle filter (VRCPF) existing in the literature by employing a kernel based filtering density function. The idea behind integrating a kernel into the model is it enables us to converge to the filtering density function smoothly resulting in improved object tracking accuracy. Video object tracking performance of the proposed filtering, K-VRCPF; has been tested on commonly used BoBoT and OTB datasets. Tracking accuracy reported in terms of center pixel error, and root mean square error (RMSE) demonstrate that, as a result of the regularized sampling of the posterior distribution, K-VRCPF with Gaussian kernels reduces the center pixel error and RMSE.
Translated title of the contribution | Robust object tracking by variable rate kernel particle filter |
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Original language | Turkish |
Title of host publication | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1-4 |
Number of pages | 4 |
ISBN (Electronic) | 9781538615010 |
DOIs | |
Publication status | Published - 5 Jul 2018 |
Event | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 - Izmir, Turkey Duration: 2 May 2018 → 5 May 2018 |
Publication series
Name | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 |
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Conference
Conference | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 |
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Country/Territory | Turkey |
City | Izmir |
Period | 2/05/18 → 5/05/18 |
Bibliographical note
Publisher Copyright:© 2018 IEEE.