Locator-Checker-Scaler Object Tracking Using Spatially Ordered and Weighted Patch Descriptor

Han Ul Kim, Chang-Su Kim

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

In this paper, we propose a simple yet effective object descriptor and a novel tracking algorithm to track a target object accurately. For the object description, we divide the bounding box of a target object into multiple patches and describe them with color and gradient histograms. Then, we determine the foreground weight of each patch to alleviate the impacts of background information in the bounding box. To this end, we perform random walk with restart (RWR) simulation. We then concatenate the weighted patch descriptors to yield the spatially ordered and weighted patch (SOWP) descriptor. For the object tracking, we incorporate the proposed SOWP descriptor into a novel tracking algorithm, which has three components: locator, checker, and scaler (LCS). The locator and the scaler estimate the center location and the size of a target, respectively. The checker determines whether it is safe to adjust the target scale in a current frame. These three components cooperate with one another to achieve robust tracking. Experimental results demonstrate that the proposed LCS tracker achieves excellent performance on recent benchmarks.

Original languageEnglish
Article number7931611
Pages (from-to)3817-3830
Number of pages14
JournalIEEE Transactions on Image Processing
Volume26
Issue number8
DOIs
Publication statusPublished - 2017 Aug 1

Keywords

  • bounding box descriptor
  • discriminative tracker
  • object tracking
  • tracking with multiple estimators
  • Visual tracking

ASJC Scopus subject areas

  • Software
  • Medicine(all)
  • Computer Graphics and Computer-Aided Design

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