Adaptive window strategy for high-speed and robust KLT feature tracker

Nirmala Ramakrishnan, Thambipillai Srikanthan, Siew Kei Lam, Gauri Ravindra Tulsulkar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)


The Kanade-Lucas-Tomasi tracking (KLT) algorithm is widely used for local tracking of features. As it employs a translation model to find the feature tracks, KLT is not robust in the presence of distortions around the feature resulting in high inaccuracies in the tracks. In this paper we show that the window size in KLT must vary to adapt to the presence of distortions around each feature point in order to increase the number of useful tracks and minimize noisy ones. We propose an adaptive window size strategy for KLT that uses the KLT iterations as an indicator of the quality of the tracks to determine near-optimal window sizes, thereby significantly improving its robustness to distortions. Our evaluations with a well-known tracking dataset show that the proposed adaptive strategy outperforms the conventional fixed-window KLT in terms of robustness. In addition, compared to the well-known affine KLT, our method achieves comparable robustness at an average runtime speedup of 7x.

Original languageEnglish
Title of host publicationImage and Video Technology - 7th Pacific-Rim Symposium, PSIVT 2015, Revised Selected Papers
PublisherSpringer Verlag
Number of pages13
ISBN (Print)9783319294506
Publication statusPublished - 01-01-2016
Externally publishedYes
Event7th Pacific-Rim Symposium on Image and Video Technology, PSIVT 2015 - Auckland, New Zealand
Duration: 25-11-201527-11-2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference7th Pacific-Rim Symposium on Image and Video Technology, PSIVT 2015
Country/TerritoryNew Zealand

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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