Abstract
The online multi-instance learning tracking (MIL) algorithm is known for its ability of alleviating tracking drift by training classifiers with positive and negative bag. However, the increased computational complexity results in time consuming due to the lack of consideration of sampling importance when collecting training samples. Additionally, the MIL method, as a 2D feature-based tracking algorithm, performs unsteadily when the object changes poses or rotates seriously. In this paper, a histogram-based feature similarity measurement is employed as a weighting strategy to select positive samples. Benefited from profitable depth information, the tracking algorithm we proposed achieves higher tracking performance. For computational efficiency, a compressive sensing method is adopted to extract features and reduce dimensionality. Experimental results demonstrate that our algorithm is better in robustness, accuracy, efficiency than three state-of-the-art methods on challenging video sequences.
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Zhao, M., Luo, H., Tafti, A.P., Lin, Y., He, G. (2015). A Hybrid Real-Time Visual Tracking Using Compressive RGB-D Features. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9474. Springer, Cham. https://doi.org/10.1007/978-3-319-27857-5_51
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DOI: https://doi.org/10.1007/978-3-319-27857-5_51
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