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Real-time object tracking via compressive feature selection

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Abstract

Recently, compressive tracking (CT) has been widely proposed for its efficiency, accuracy and robustness on many challenging sequences. Its appearance model employs non-adaptive random projections that preserve the structure of the image feature space. A very sparse measurement matrix is used to extract features by multiplying it with the feature vector of the image patch. An adaptive Bayes classifier is trained using both positive samples and negative samples to separate the target from background. On the CT framework, however, some features used for classification have weak discriminative abilities, which reduces the accuracy of the strong classifier. In this paper, we present an online compressive feature selection algorithm(CFS) based on the CT framework. It selects the features which have the largest margin when using them to classify positive samples and negative samples. For features that are not selected, we define a random learning rate to update them slowly. It makes those weak classifiers preserve more target information, which relieves the drift when the appearance of the target changes heavily. Therefore, the classifier trained with those discriminative features couples its score in many challenging sequences, which leads to a more robust tracker. Numerous experiments show that our tracker could achieve superior result beyond many state-of-the-art trackers.

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Authors and Affiliations

  1. School of Computer Science, Wuhan University, Wuhan, 430072, China

    Kang Li, Fazhi He & Xiao Chen

Authors
  1. Kang Li
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  2. Fazhi He
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  3. Xiao Chen
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Corresponding author

Correspondence to Fazhi He.

Additional information

Kang Li received the MS degree in computer science from Central China Normal University, China in 2012. He is currently a PhD candidate in School of Computer Science, Wuhan University, China. His research interests are pattern recognition, image processing, and computer vision.

Fazhi He received his PhD degree from Wuhan University of Technology, China. Now he is a professor in School of Computer, Wuhan University, China. His research interests are computer graphics, computer-aided design, image processing and computer supported cooperative work.

Xiao Chen received the MS degree in computer science from Three Gorges University, China in 2010. He is currently a PhD candidate in the School of Computer Science, Wuhan University, China. His research interests are machine learning, image matting, and computer vision.

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Li, K., He, F. & Chen, X. Real-time object tracking via compressive feature selection. Front. Comput. Sci. 10, 689–701 (2016). https://doi.org/10.1007/s11704-016-5106-5

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  • DOI: https://doi.org/10.1007/s11704-016-5106-5

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