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• W2802746957 abstract "Visual object tracking is a fundamental task in computer vision with its wide range of applications. In this paper, we propose a robust algorithm based on the kernelized correlation filter framework to handle occlusions or scale variations. Our algorithm takes into account the relationships between the target object and its surrounding context, and learns a discriminative correlation filter for the estimation of the new position. Another discriminative regression model via constructing the target pyramid is introduced to estimate the optimal scale. The proposed algorithm integrated with two discriminative regression models can track complex targets with occlusion and deformation at real-time. The competitive experimental results on the dataset sequences show that the proposed tracker outperforms other state-of-the-art methods, in both the precision and the success rate." @default.
• W2802746957 created "2018-05-17" @default.
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• W2802746957 date "2018-01-01" @default.
• W2802746957 modified "2023-10-14" @default.
• W2802746957 title "Learning Temporal Context for Correlation Tracking with Scale Estimation" @default.
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• W2802746957 doi "https://doi.org/10.1007/978-3-319-77380-3_72" @default.
• W2802746957 hasPublicationYear "2018" @default.
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