Abstract
To solve the problem of significant occlusion and failure when reappearing in combining Kalman filter and Mean Shift, a new improved method which is based on Kalman filter and Mean Shift was proposed. In the algorithm, first, the parameter of Bhattacharyya is used to scale the degree of occlusion, then Kalman filter or linear prediction was chosen to update the searching-loop point of Mean Shift according to the Bhattacharyya parameter. The experiment results indicate that the searching and tracking time can be reduced down 9.68% and 17.58%. A continuous and stable tracking results can be obtained in the situation of significant occlusion and re-appearance.
| Original language | English |
|---|---|
| Pages (from-to) | 1056-1061 |
| Number of pages | 6 |
| Journal | Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology |
| Volume | 33 |
| Issue number | 10 |
| Publication status | Published - 2013 |
Keywords
- Kalman filter
- Linear prediction
- Mean Shift algorithm
- Occlusion estimation
- Realtime
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Zhang, X. J., Chen, H., & Yang, J. (2013). Anti-occlusion tracking algorithm combined Kalman filter and Mean Shift. Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology, 33(10), 1056-1061.