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Real-time multi-scale parallel compressive tracking

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Abstract

Robust visual tracking is a challenging problem because the appearance of a target may rapidly change due to significant variations in the object’s motion and the surrounding illumination. In this paper, a novel robust visual tracking algorithm is proposed based on an existing compressive tracking method. The proposed algorithm adopts multiple naive Bayes classifiers, each trained under a different scale condition, to realize online parallel multi-scale classification. Further, each classifier was initialized by randomly generating different types of Haar-like features. By doing so, the robustness of the feature classification can be improved to obtain more accurate tracking results. To enhance the real-time performance of the visual tracking system, the formula of the naive Bayes classifier is studied and simplified to speed up the processing speed of parallel multi-scale feature classification. After acceleration via formula simplification and parallel implementation, the proposed visual tracking algorithm can reach a tracking performance of approximately 45 frames per second (fps) when dealing with images of 642 × 352 pixels on a popular Intel Core i5-3230M platform. The experimental results show that the proposed algorithm outperforms state-of-the-art visual tracking methods on challenging videos in terms of success rate, tracking accuracy, and visual comparison.

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Acknowledgements

This work was supported by the Ministry of Science and Technology of Taiwan, ROC under grant MOST 105-2221-E-032-024 and 103-2632-E-032-001-MY3.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Tamkang University, 151 Yingzhuan Road, Tamsui District, New Taipei City, 251, Taiwan, ROC

    Chi-Yi Tsai & Yen-Chang Feng

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  1. Chi-Yi Tsai
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  2. Yen-Chang Feng
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Correspondence to Chi-Yi Tsai.

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Tsai, CY., Feng, YC. Real-time multi-scale parallel compressive tracking. J Real-Time Image Proc 16, 2073–2091 (2019). https://doi.org/10.1007/s11554-017-0713-4

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  • DOI: https://doi.org/10.1007/s11554-017-0713-4

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