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Real-time multi-scale tracking based on compressive sensing

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Abstract

Tracking-by-detection methods have been widely studied and some promising results have been obtained. These methods use discriminative appearance models to train and update online classifiers. They also use a sliding window to detect samples which will then be classified. Then, the location of the sample with the maximum classifier response will be selected as the new location. Compressive tracking was recently proposed with an appearance model based on features extracted in the compressed domain. However, CT uses a fixed-size tracking box to detect samples, and this is unsuitable for practice applications. CT detects samples around the selected region of the previous frame within a fixed radius. Here, the classifier may become inaccurate if the selected region drifts. The fixed radius is also not suitable for tracking targets that experience abrupt acceleration changes. Furthermore, CT updates the classifier parameters with constant learning rate. If the target is fully occluded for an extended period, the classifier will instead learn the features of the cover object and the target will ultimately be lost. In this paper, we present a multi-scale compressive tracker. This tracker integrates an improved appearance model based on normalized rectangle features extracted in the adaptive compressive domain into the bootstrap filter. This type of feature extraction is efficient, and the computation complexity does not increase as the tracking regions become larger. The classifier response is utilized to generate particle importance weight and a re-sample procedure preserves samples according to weight. A 2-order transition model considers the target velocity to estimate the current position and scale status. In this way, the sampling is not limited to a fixed range. Here, feedback strategies are adopted to adjust learning rate for occlusion. Experimental results on various benchmark challenging sequences have demonstrated the superior performance of our tracker when compared with several state-of-the-art tracking algorithms.

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Notes

  1. http://visual-tracking.net.

  2. http://vision.ucsd.edu/~bbabenko/project_miltrack.shtml.

  3. http://www4.comp.polyu.edu.hk/~cslzhang/CT/CT.htm.

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Acknowledgments

This research work was supported by the National Natural Science Foundation of China (Grant Nos. 51074169 and 51134024), the National High Technology Research and Development Program of China (863 Program) (Grant No. 2012AA062203). The authors would like to thank the anonymous reviewers for their helpful comments and suggestions.

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  1. School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing , 100083, China

    Yunxia Wu, Ni Jia & Jiping Sun

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  1. Yunxia Wu
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  2. Ni Jia
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Correspondence to Ni Jia.

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Wu, Y., Jia, N. & Sun, J. Real-time multi-scale tracking based on compressive sensing. Vis Comput 31, 471–484 (2015). https://doi.org/10.1007/s00371-014-0942-5

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