Real-time tracking-with-detection for coping with viewpoint change

Shaul Oron, Aharon Bar-Hillel, Shai Avidan

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

We consider real-time visual tracking with targets undergoing viewpoint changes. The problem is evaluated on a new and extensive dataset of vehicles undergoing large viewpoint changes. We propose an evaluation method in which tracking accuracy is measured under real-time computational complexity constraints and find that state-of-the-art agnostic trackers, as well as class detectors, are still struggling with this task. We study tracking schemes fusing real-time agnostic trackers with a non-real-time class detector used for template update, with two dominating update strategies emerging. We rigorously analyze the template update latency and demonstrate that such methods significantly outperform stand-alone trackers and class detectors. Results are demonstrated using two different trackers and a state-of-the-art classifier, and at several operating points of algorithm/hardware computational speed.

Original languageEnglish
Pages (from-to)507-518
Number of pages12
JournalMachine Vision and Applications
Volume26
Issue number4
DOIs
StatePublished - 1 May 2015
Externally publishedYes

Keywords

  • Detection
  • Fusion
  • Real time
  • Tracking

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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