Please use this identifier to cite or link to this item:
|Title:||Data-driven probability hypothesis density filter for visual tracking|
Probability hypothesis density (PHD)
Sequential Monte Carlo method
|Source:||Wang, Y.-D., Wu, J.-K., Kassim, A.A., Huang, W. (2008-08). Data-driven probability hypothesis density filter for visual tracking. IEEE Transactions on Circuits and Systems for Video Technology 18 (8) : 1085-1095. ScholarBank@NUS Repository. https://doi.org/10.1109/TCSVT.2008.927105|
|Abstract:||We apply the probability hypothesis density (PHD) filter to track a random number of pedestrians in image sequences. The PHD filter is implemented using particle filter. How to design importance functions of the particle PHD filter remains a challenge, especially when targets can appear, disappear, merge, or split at any time. To meet this challenge, we have modeled the targets into two categories: survival objects and spontaneous birth objects. Based on the model, we have derived the data-driven importance function for a particle PHD filter and applied to pedestrians tracking where people or groups appear, merge, split, and disappear in the field of view of a camera. The experimental results have demonstrated the effectiveness of the particle PHD filter using the proposed importance function in tracking random number of pedestrians and deriving their locations. © 2008 IEEE.|
|Source Title:||IEEE Transactions on Circuits and Systems for Video Technology|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 14, 2017
WEB OF SCIENCETM
checked on Nov 17, 2017
checked on Dec 10, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.