ML Anthology

Adaptive and Discriminative Metric Differential Tracking

Nan Jiang, Wenyu Liu, Ying Wu
CVPR 2011 pp. 1161-1168
doi:10.1109/CVPR.2011.5995716 /cvpr/2011/jiang2011cvpr-adaptive/

Abstract

Matching the visual appearances of the target over consecutive image frames is the most critical issue in video-based object tracking. Choosing an appropriate distance metric for matching determines its accuracy and robustness, and significantly influences the tracking performance. This paper presents a new tracking approach that incorporates adaptive metric into differential tracking method. This new approach automatically learns an optimal distance metric for more accurate matching, and obtains a closed-form analytical solution to motion estimation and differential tracking. Extensive experiments validate the effectiveness of adaptive metric, and demonstrate the improved performance of the proposed new tracking method.

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Cite

Text

Jiang et al. "Adaptive and Discriminative Metric Differential Tracking." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2011. doi:10.1109/CVPR.2011.5995716

Markdown

[Jiang et al. "Adaptive and Discriminative Metric Differential Tracking." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2011.](https://mlanthology.org/cvpr/2011/jiang2011cvpr-adaptive/) doi:10.1109/CVPR.2011.5995716

BibTeX

@inproceedings{jiang2011cvpr-adaptive,
  title     = {{Adaptive and Discriminative Metric Differential Tracking}},
  author    = {Jiang, Nan and Liu, Wenyu and Wu, Ying},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2011},
  pages     = {1161-1168},
  doi       = {10.1109/CVPR.2011.5995716},
  url       = {https://mlanthology.org/cvpr/2011/jiang2011cvpr-adaptive/}
}