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Tracking using Multilevel Quantizations

2014

Conference Paper

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Most object tracking methods only exploit a single quantization of an image space: pixels, superpixels, or bounding boxes, each of which has advantages and disadvantages. It is highly unlikely that a common optimal quantization level, suitable for tracking all objects in all environments, exists. We therefore propose a hierarchical appearance representation model for tracking, based on a graphical model that exploits shared information across multiple quantization levels. The tracker aims to find the most possible position of the target by jointly classifying the pixels and superpixels and obtaining the best configuration across all levels. The motion of the bounding box is taken into consideration, while Online Random Forests are used to provide pixel- and superpixel-level quantizations and progressively updated on-the-fly. By appropriately considering the multilevel quantizations, our tracker exhibits not only excellent performance in non-rigid object deformation handling, but also its robustness to occlusions. A quantitative evaluation is conducted on two benchmark datasets: a non-rigid object tracking dataset (11 sequences) and the CVPR2013 tracking benchmark (50 sequences). Experimental results show that our tracker overcomes various tracking challenges and is superior to a number of other popular tracking methods.

Author(s): Zhibin Hong and Chaohui Wang and Xue Mei and Danil Prokhorov and Dacheng Tao
Book Title: Computer Vision – ECCV 2014
Volume: 8694
Pages: 155--171
Year: 2014
Month: September

Series: Lecture Notes in Computer Science
Editors: D. Fleet and T. Pajdla and B. Schiele and T. Tuytelaars
Publisher: Springer International Publishing

Department(s): Perzeptive Systeme
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

DOI: 10.1007/978-3-319-10599-4_11
Event Name: 13th European Conference on Computer Vision
Event Place: Zürich, Switzerland
Attachments: pdf

BibTex

@inproceedings{HongWMPT:ECCV2014,
  title = {Tracking using Multilevel Quantizations},
  author = {Hong, Zhibin and Wang, Chaohui and Mei, Xue and Prokhorov, Danil and Tao, Dacheng},
  booktitle = {Computer Vision -- ECCV 2014},
  volume = {8694},
  pages = {155--171},
  series = {Lecture Notes in Computer Science},
  editors = {D. Fleet  and T. Pajdla and B. Schiele  and T. Tuytelaars },
  publisher = {Springer International Publishing},
  month = sep,
  year = {2014},
  month_numeric = {9}
}