Name
Abstract | ||
|---|---|---|
This paper proposes a robust tracking algorithm by third-order tensor representation and adaptive appearance modeling. In this method, the target in each video frame is represented by a third-order tensor. This representation preserves the spatial correlation inside the target region and can integrate multiple appearance cues for target description. Based on this representation, a multilinear subspace is learned online to model the target appearance variations during tracking. Compared to other methods, our approach can detect local spatial structure in the target tensor space and fuse information from different feature spaces. Therefore, the learned appearance model is more discriminative when there are significant appearance variations of the target or when the background gets cluttered. Applying the multilinear algebra, our appearance model can efficiently be learned and updated online, without causing high-dimensional data-learning problems. Then, tracking is implemented in the Bayesian inference framework, where a likelihood model is defined to measure the similarity between a test sample and the learned appearance model, and a particle filter is used to recursively estimate the target state over time. Theoretic analysis and experiments compared with other state-of-the-art methods demonstrate the effectiveness of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/TSMCB.2010.2056366 | IEEE Transactions on Systems, Man, and Cybernetics, Part B |
Keywords | DocType | Volume |
multiple appearance cue,particle filtering (numerical methods),image representation,multilinear algebra,target tensor space,target description,adaptive appearance modeling,inference mechanisms,learning (artificial intelligence),information fusion,local spatial structure detection,third-order tensor representation,particle filter,target region,robust tracking algorithm,likelihood model,internet,appearance model,bayes methods,bayesian inference framework,object tracking,appearance variations,target representation,learned appearance model,target state,recursive estimation,multilinear subspace learning,significant appearance variation,target appearance variation,multilinear subspace,tensors,sensor fusion,video frame,tensile stress,fuses,bayesian methods,algebra,learning artificial intelligence,testing,time measurement,algorithms,bayesian method,artificial intelligence,robustness,photography | Journal | 41 |
Issue | ISSN | Citations |
2 | 1941-0492 | 11 |
PageRank | References | Authors |
0.52 | 21 | 3 |