Name
Title | ||
|---|---|---|
Adaptive particle sampling and adaptive appearance for multiple video object tracking |
Abstract | ||
|---|---|---|
In this work, we propose an innovative method to integrate the Kalman filter and adaptive particle sampling for multiple video object tracking. Taking advantage of both the closed-form equations for optimal prediction and update from Kalman filters and the versatility of particle sampling for measurement selection under occlusion or segmentation error cases, the proposed method achieves both high tracking accuracy and computational simplicity. The adaptive particle sampling, which uses parameters updated by Kalman filters, can thus require only a small number of particles to achieve high positioning and scaling accuracy. Also, the concept of adaptive appearance is applied to enhance the robustness of occlusion handling. The experimental results confirm the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.sigpro.2009.03.034 | Signal Processing |
Keywords | Field | DocType |
Kalman filter,Adaptive particle sampling,Adaptive appearance,Tracking,Occlusion handling | Small number,Computer vision,Segmentation,Control theory,Computer science,Closed-form expression,Kalman filter,Robustness (computer science),Video tracking,Sampling (statistics),Artificial intelligence,Particle | Journal |
Volume | Issue | ISSN |
89 | 9 | 0165-1684 |
Citations | PageRank | References |
20 | 0.89 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hsu-Yung Cheng | 1 | 243 | 23.56 |
| Jenq-Neng Hwang | 2 | 1675 | 206.57 |