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- W2897744204 abstract "Recently1, a class of method with correlation filtering for target tracking is one of the hottest methods in the tracking field. However, the performance of visual tracking still needs to be improved. Consequently, a scheme employing a key-point-based strategy to solve the multi-scale problem is proposed. An explicit scale filter is learned online using fore-and-backwards optical flow method which matches the reserved reliable points pairs at different scales. Moreover, these sparse pairs of tracked points are still adopted to discriminate occlusion. An extensive evaluation on OTB-2013 with 50 public test sequences was conducted. Experimental data and analysis indicate that our approach exceeds some other advanced tracking methods; there is a considerable improvement in speed. Additionally, our method can achieve more competitive performance even in the case where scale variation or partial occlusion occurs." @default.
- W2897744204 created "2018-10-26" @default.
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- W2897744204 date "2018-10-22" @default.
- W2897744204 modified "2023-10-16" @default.
- W2897744204 title "A Fast Scale Adaptive Kernel Correlation Filter Tracker via Reliable Key Points" @default.
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- W2897744204 doi "https://doi.org/10.1145/3207677.3277926" @default.
- W2897744204 hasPublicationYear "2018" @default.
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