Name
Abstract | ||
|---|---|---|
We present in this paper a new visual tracking framework based on the MCMC-based particle algorithm. Firstly, in order to obtain a more informative likelihood, we propose to combine the color-based observation model with a detection confidence density obtained from the Histograms of Oriented Gradients (HOG) descriptor. The MCMC-based particle algorithm is then employed to estimate the posterior distribution of the target state to solve the tracking problem. The global system has been tested on different real datasets. Experimental results demonstrate the robustness of the proposed system in several difficult scenarios. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICASSP.2012.6288173 | Acoustics, Speech and Signal Processing |
Keywords | Field | DocType |
image colour analysis,particle filtering (numerical methods),HOG descriptor,MCMC-based particle algorithm,MCMC-based particle filtering,color-based observation model,detection confidence density,different real datasets,global system,histograms of oriented gradients,informative likelihood,posterior distribution estimation,tracking problem,visual tracking framework,HOG,MCMC,Visual tracking,particle filtering | Histogram,Pattern recognition,Markov chain Monte Carlo,Computer science,Visualization,Particle filter,Posterior probability,Robustness (computer science),Eye tracking,Artificial intelligence,Covariance matrix | Conference |
ISSN | ISBN | Citations |
1520-6149 E-ISBN : 978-1-4673-0044-5 | 978-1-4673-0044-5 | 5 |
PageRank | References | Authors |
0.41 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cong, D.-N.T. | 1 | 5 | 0.41 |
| Septier, F. | 2 | 5 | 0.41 |
| Garnier, C. | 3 | 7 | 1.37 |
| Khoudour, L. | 4 | 5 | 0.41 |