Severely Blurred Object Tracking by Learning Deep Image Representations

An implicit assumption in many generic object trackers is that the videos are blur free. However, motion blur is very common in real videos. The performance of a generic object tracker may drop significantly when it is applied to videos with severe motion blur. In this paper, we propose a new Tracki...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology 2016-02, Vol.26 (2), p.319-331
Main Authors: Ding, Jianwei, Huang, Yongzhen, Liu, Wei, Huang, Kaiqi
Format: Article
Language:English
Subjects:
Algorithm design and analysis
Circuits
Data transfer (computers)
deep learning
Learning
Object tracking
Parameter estimation
Representations
Robustness
severe blur
Software reviews
Target tracking
Tracking
Training
Videos
Visual
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Summary:An implicit assumption in many generic object trackers is that the videos are blur free. However, motion blur is very common in real videos. The performance of a generic object tracker may drop significantly when it is applied to videos with severe motion blur. In this paper, we propose a new Tracking-Learning-Data approach to transfer a generic object tracker to a blur-invariant object tracker without deblurring image sequences. Before object tracking, a large set of unlabeled images is used to learn objects' visual prior knowledge, which is then transferred to the appearance model of a specific target. During object tracking, online training samples are collected from the tracking results and the context information. Different blur kernels are involved with the training samples to increase the robustness of the appearance model to severe blur, and the motion parameters of the object are estimated in the particle filter framework. Extensive experimental results demonstrate that the proposed algorithm can robustly track objects not only in severely blurred videos but also in other challenging scenes.
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2015.2406231