Abstract
In this paper, we address a real-time object tracking algorithm considering local binary pattern (LBP) as a feature descriptor. In addition to texture feature, Ohta color features are included in the feature vector of the covariance tracking algorithm. The performance of the proposed algorithm is compared with some other competitive object tracking algorithms such as the RGB feature-based covariance method and color histogram method. The comparisons of the performance among these algorithms include detection rate and computational time. These methods have been applied to four different challenging situations, and the resulting experimental results show the robustness of the proposed technique against occlusion, camera motion, appearance, and change in illumination condition.
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Dash, P.P., Patra, D., Mishra, S.K. (2014). Local Binary Pattern as a Texture Feature Descriptor in Object Tracking Algorithm. In: Mohapatra, D.P., Patnaik, S. (eds) Intelligent Computing, Networking, and Informatics. Advances in Intelligent Systems and Computing, vol 243. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1665-0_52
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DOI: https://doi.org/10.1007/978-81-322-1665-0_52
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-1664-3
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