Name
Abstract | ||
|---|---|---|
This paper deals with region-of-interest (ROI) tracking in video sequences. The goal is to determine in successive frames the region which best matches, in terms of a similarity measure, a ROI defined in a reference frame. Some tracking methods define similarity measures which efficiently combine several visual features into a probability density function (PDF) representation, thus building a discriminative model of the ROI. This approach implies dealing with PDFs with domains of definition of high dimension. To overcome this obstacle, a standard solution is to assume independence between the different features in order to bring out low-dimension marginal laws and/or to make some parametric assumptions on the PDFs at the cost of generality. We discard these assumptions by proposing to compute the Kullback-Leibler divergence between high-dimensional PDFs using the k th nearest neighbor framework. In consequence, the divergence is expressed directly from the samples, i.e., without explicit estimation of the underlying PDFs. As an application, we defined 5, 7, and 13-dimensional feature vectors containing color information (including pixel-based, gradient-based and patch-based) and spatial layout. The proposed procedure performs tracking allowing for translation and scaling of the ROI. Experiments show its efficiency on a movie excerpt and standard test sequences selected for the specific conditions they exhibit: partial occlusions, variations of luminance, noise, and complex motion. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TIP.2009.2015158 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
image sequences,parameter estimation,probability,statistical analysis,tracking,K-th nearest neighbor,Kullback-Leibler divergence,high-dimensional statistical measure,low-dimension marginal laws,nonparametric estimation,probability density function,reference frame,region-of-interest tracking,standard test sequences,video sequences,$k$th nearest neighbor,High-dimensional probability density function (PDF),Kullback–Leibler divergence,nonparametric estimation,region-of-interest (ROI) tracking | Reference frame,Similitude,Feature vector,Pattern recognition,Similarity measure,Parametric statistics,Artificial intelligence,Region of interest,Estimation theory,Mathematics,Kullback–Leibler divergence | Journal |
Volume | Issue | ISSN |
18 | 6 | 1057-7149 |
Citations | PageRank | References |
8 | 0.64 | 27 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sylvain Boltz | 1 | 46 | 5.61 |
| Eric Debreuve | 2 | 260 | 21.54 |
| Michel Barlaud | 3 | 2317 | 310.53 |