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Probabilistic Index Histogram for Robust Object Tracking

  • Wei Li
  • Xiaoqin Zhang
  • Nianhua Xie
  • Weiming Hu
  • Wenhan Luo
  • Haibin Ling
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6468)

Abstract

Color histograms are widely used for visual tracking due to their robustness against object deformations. However, traditional histogram representation often suffers from problems of partial occlusion, background cluttering and other appearance corruptions. In this paper, we propose a probabilistic index histogram to improve the discriminative power of the histogram representation. With this modeling, an input frame is translated into an index map whose entries indicate indexes to a separate bin. Based on the index map, we introduce spatial information and the bin-ratio dissimilarity in histogram comparison. The proposed probabilistic indexing technique, together with the two robust measurements, greatly increases the discriminative power of the histogram representation. Both qualitative and quantitative evaluations show the robustness of the proposed approach against partial occlusion, noisy and clutter background.

Keywords

Spatial Distance Color Histogram Visual Tracking Tracking Result Partial Occlusion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Wei Li
    • 1
  • Xiaoqin Zhang
    • 2
  • Nianhua Xie
    • 1
  • Weiming Hu
    • 1
  • Wenhan Luo
    • 1
  • Haibin Ling
    • 3
  1. 1.National Lab of Pattern RecognitionInstitute of Automation, CASBeijingChina
  2. 2.College of Mathematics & Information ScienceWenzhou UniversityZhejiangChina
  3. 3.Dept. of Computer and Information SciencesTemple UniversityUSA

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