NUS-PRO: A New Visual Tracking Challenge

Annan Li, Min Lin, Yi Wu, Ming Hsuan Yang, Shuicheng Yan

Research output: Contribution to journalArticle

75 Citations (Scopus)

Abstract

Numerous approaches on object tracking have been proposed during the past decade with demonstrated success. However, most tracking algorithms are evaluated on limited video sequences and annotations. For thorough performance evaluation, we propose a large-scale database which contains 365 challenging image sequences of pedestrians and rigid objects. The database covers 12 kinds of objects, and most of the sequences are captured from moving cameras. Each sequence is annotated with target location and occlusion level for evaluation. A thorough experimental evaluation of 20 state-of-the-art tracking algorithms is presented with detailed analysis using different metrics. The database is publicly available and evaluation can be carried out online for fair assessments of visual tracking algorithms.

Original languageEnglish
Article number7072555
Pages (from-to)335-349
Number of pages15
JournalIEEE transactions on pattern analysis and machine intelligence
Volume38
Issue number2
DOIs
Publication statusPublished - 2016 Feb 1

    Fingerprint

All Science Journal Classification (ASJC) codes

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

Cite this