Tampere University of Technology

TUTCRIS Research Portal

Long-term tracking through failure cases

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publicationProceedings - 2013 IEEE International Conference on Computer Vision Workshops, ICCVW 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages153-160
Number of pages8
ISBN (Print)9781479930227
DOIs
Publication statusPublished - 2013
Publication typeA4 Article in a conference publication
Event2013 14th IEEE International Conference on Computer Vision Workshops, ICCVW 2013 - Sydney, NSW, Australia
Duration: 1 Dec 20138 Dec 2013

Conference

Conference2013 14th IEEE International Conference on Computer Vision Workshops, ICCVW 2013
CountryAustralia
CitySydney, NSW
Period1/12/138/12/13

Abstract

Long term tracking of an object, given only a single instance in an initial frame, remains an open problem. We propose a visual tracking algorithm, robust to many of the difficulties which often occur in real-world scenes. Correspondences of edge-based features are used, to overcome the reliance on the texture of the tracked object and improve invariance to lighting. Furthermore we address long-term stability, enabling the tracker to recover from drift and to provide redetection following object disappearance or occlusion. The two-module principle is similar to the successful state-of-the-art long-term TLD tracker, however our approach extends to cases of low-textured objects. Besides reporting our results on the VOT Challenge dataset, we perform two additional experiments. Firstly, results on short-term sequences show the performance of tracking challenging objects which represent failure cases for competing state-of-the-art approaches. Secondly, long sequences are tracked, including one of almost 30000 frames which to our knowledge is the longest tracking sequence reported to date. This tests the re-detection and drift resistance properties of the tracker. All the results are comparable to the state-of-the-art on sequences with textured objects and superior on non-textured objects. The new annotated sequences are made publicly available.

Keywords

  • Computer vision, Edge, Line correspondence, Long-term tracking, Low texture, Visual tracking