NDLI: Long-Term Tracking through Failure Cases

Content Provider	IEEE Xplore Digital Library
Author	Lebeda, K. Hadfield, S. Matas, J. Bowden, R.
Copyright Year	2013
Description	Author affiliation: CVSSP, Univ. of Surrey, Guildford, UK (Lebeda, K.; Hadfield, S.; Bowden, R.) \|\| CMP, Czech Tech. Univ., Prague, Czech Republic (Matas, J.)
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.
Starting Page	153
Ending Page	160
File Size	963142
Page Count	8
File Format	PDF
ISBN	9781479930227
DOI	10.1109/ICCVW.2013.26
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-02
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Visualization Computer vision Target tracking Visual tracking Image edge detection Lighting Apertures Line correspondence Robustness Long-term tracking Edge Low texture
Content Type	Text
Resource Type	Article

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