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| Content Provider | IET Digital Library |
|---|---|
| Author | Kumar, Pankaj Dick, Anthony |
| Abstract | This study describes a complete system for multiple-target tracking in image sequences. The target appearance is represented as a set of weighted clusters in colour space. This is in contrast to the more typical use of colour histograms to model target appearance. The use of clusters allows a more flexible and accurate representation of the target, which demonstrates the benefits for tracking. However, it also introduces a number of computational difficulties, as calculating and matching cluster signatures are both computationally intensive tasks. To overcome this, the authors introduce a new formulation of incremental medoid-shift clustering that operates faster than mean shift in multi-target tracking scenarios. This matching scheme is integrated into a Bayesian tracking framework. Particle filters, a special case of Bayesian filters where the state variables are non-linear and non-Gaussian, are used in this study. An adaptive model update procedure is proposed for the cluster signature representation to handle target changes with time. The model update procedure is demonstrated to work successfully on a synthetic dataset and then on real datasets. Successful tracking results are shown on public datasets. Both qualitative and quantitative evaluations have been carried out to demonstrate the improved performance of the proposed multi-target tracking system. A higher tracking accuracy in long image sequences has been achieved compared to other standard tracking methods. |
| Starting Page | 246 |
| Ending Page | 257 |
| Page Count | 12 |
| ISSN | 17519632 |
| Volume Number | 7 |
| e-ISSN | 17519640 |
| Issue Number | Issue 4, Aug (2013) |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-cvi/7/4 |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2011.0223 |
| Journal | IET Computer Vision |
| Access Restriction | Open |
| Rights Holder | © The Institution of Engineering and Technology |
| Subject Keyword | Adaptive Earth Mover Distance-based Bayesian Multitarget Tracking Adaptive Model Update Procedure Bayesian Filter Cluster Signature Matching Cluster Signature Representation Colour Histograms Colour Space Computer Vision And Image Processing Technique Filtering Method in Signal Processing Image Colour Analysis Image Matching Image Recognition Image Representation Image Sequence Incremental Medoid-shift Clustering Mean Shift NonGaussian State Variables Nonlinear State Variables Particle Filter Synthetic Dataset Target Appearance Representation Target Tracking Tracking Accuracy Tracking Filter Weighted Clusters |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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