Robust Object Tracking via Locality Sensitive Histograms

Shengfeng He, Rynson W.H. Lau, Qingxiong Yang, Jiang Wang, Ming Hsuan Yang

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

This paper presents a novel locality sensitive histogram (LSH) algorithm for visual tracking. Unlike the conventional image histogram that counts the frequency of occurrence of each intensity value by adding ones to the corresponding bin, an LSH is computed at each pixel location, and a floating-point value is added to the corresponding bin for each occurrence of an intensity value. The floating-point value exponentially reduces with respect to the distance to the pixel location where the histogram is computed. An efficient algorithm is proposed that enables the LSHs to be computed in time linear in the image size and the number of bins. In addition, this efficient algorithm can be extended to exploit color images. A robust tracking framework based on the LSHs is proposed, which consists of two main components: a new feature for tracking that is robust to illumination change and a novel multiregion tracking algorithm that runs in real time even with hundreds of regions. Extensive experiments demonstrate that the proposed tracking framework outperforms the state-of-the-art methods in challenging scenarios, especially when the illumination changes dramatically. Evaluation using the latest benchmark shows that our algorithm is the top performer.

Original languageEnglish
Article number7401033
Pages (from-to)1006-1017
Number of pages12
JournalIEEE Transactions on Circuits and Systems for Video Technology
Volume27
Issue number5
DOIs
Publication statusPublished - 2017 May

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All Science Journal Classification (ASJC) codes

  • Media Technology
  • Electrical and Electronic Engineering

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