ECCV 2014: Computer Vision – ECCV 2014 pp 155-171 | Cite as

Tracking Using Multilevel Quantizations

  • Zhibin Hong
  • Chaohui Wang
  • Xue Mei
  • Danil Prokhorov
  • Dacheng Tao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8694)

Abstract

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.

Keywords

Tracking Multilevel Quantizations Online Random Forests Non-rigid Object Tracking Conditional Random Fields 
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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Zhibin Hong
    • 1
  • Chaohui Wang
    • 2
  • Xue Mei
    • 3
  • Danil Prokhorov
    • 3
  • Dacheng Tao
    • 1
  1. 1.Centre for Quantum Computation and Intelligent Systems, Faculty of Engineering and Information TechnologyUniversity of TechnologySydneyAustralia
  2. 2.Max Planck Institute for Intelligent SystemsTübingenGermany
  3. 3.Toyota Research Institute, North AmericaAnn ArborUSA

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