Long-Term Tracking Through Failure Cases

Lebeda, K, Hadfield, S, Matas, J and Bowden, R (2013) Long-Term Tracking Through Failure Cases In: IEEE workshop on visual object tracking challenge at ICCV, 2013-12-01-2013-12-08, Sydney, NSW.

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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 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

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 :	Lebeda, K, Hadfield, S, Matas, J and Bowden, R
Date :	2 December 2013
DOI :	10.1109/ICCVW.2013.26
Contributors :	Contribution Name Email ORCID http://www.loc.gov/loc.terms/relators/PBL IEEE,
Related URLs :	http://personal.ee.surrey.ac.uk/Personal...
Additional Information :	This ICCV2013 Workshop paper is the Open Access version, provided by the Computer Vision Foundation. The authoritative version of this paper is available at http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6755891&tag=1
Depositing User :	Symplectic Elements
Date Deposited :	18 Nov 2015 10:42
Last Modified :	06 Jul 2019 05:15
URI:	http://epubs.surrey.ac.uk/id/eprint/808958

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