Context-aware visual tracking

Ming Yang*, Ying Wu, Gang Hua

*Corresponding author for this work

Research output: Contribution to journalArticle

186 Scopus citations

Abstract

Enormous uncertainties in unconstrained environments lead to a fundamental dilemma that many tracking algorithms have to face in practice: Tracking has to be computationally efficient, but verifying whether or not the tracker is following the true target tends to be demanding, especially when the background is cluttered and/or when occlusion occurs. Due to the lack of a good solution to this problem, many existing methods tend to be either effective but computationally intensive by using sophisticated image observation models or efficient but vulnerable to false alarms. This greatly challenges long-duration robust tracking. This paper presents a novel solution to this dilemma by considering the context of the tracking scene. Specifically, we integrate into the tracking process a set of auxiliary objects that are automatically discovered in the video on the fly by data mining. Auxiliary objects have three properties, at least in a short time interval: 1) persistent co-occurrence with the target, 2) consistent motion correlation to the target, and 3) easy to track. Regarding these auxiliary objects as the context of the target, the collaborative tracking of these auxiliary objects leads to efficient computation as well as strong verification. Our extensive experiments have exhibited exciting performance in very challenging real-world testing cases.

Original languageEnglish (US)
Pages (from-to)1195-1209
Number of pages15
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume31
Issue number7
DOIs
StatePublished - Apr 21 2009

Keywords

  • Belief inconsistency
  • Collaborative tracking
  • Computer vision
  • Context aware
  • Data mining
  • Robust fusion
  • Visual object tracking

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Context-aware visual tracking'. Together they form a unique fingerprint.

  • Cite this