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Long-Term Tracking Through Failure Cases

Lebeda, Karel, Hadfield, Simon, Matas, Jiri and Bowden, Richard (2013) Long-Term Tracking Through Failure Cases In: ICCV workshop on Visual Object Tracking Challenge, 2 - 8 Dec 2013, Sydney, Australia.

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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 30 000 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.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
AuthorsEmailORCID
Lebeda, Karelk.lebeda@surrey.ac.ukUNSPECIFIED
Hadfield, Simons.hadfield@surrey.ac.ukUNSPECIFIED
Matas, Jirimatas@cmp.felk.cvut.czUNSPECIFIED
Bowden, Richardr.bowden@surrey.ac.ukUNSPECIFIED
Date : 2 December 2013
Identification Number : 10.1109/ICCVW.2013.26
Uncontrolled Keywords : computer vision, visual tracking, long-term tracking, low texture, edge, line correspondence
Additional Information : © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Depositing User : Karel Lebeda
Date Deposited : 11 Feb 2015 14:32
Last Modified : 11 Feb 2015 14:32
URI: http://epubs.surrey.ac.uk/id/eprint/804939

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