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Title: Data-driven probability hypothesis density filter for visual tracking
Authors: Wang, Y.-D.
Wu, J.-K.
Kassim, A.A. 
Huang, W.
Keywords: Particle filter
Probability hypothesis density (PHD)
Sequential Monte Carlo method
Visual tracking
Issue Date: Aug-2008
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.
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
ISSN: 10518215
DOI: 10.1109/TCSVT.2008.927105
Appears in Collections:Staff Publications

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