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Hybrid Cascade Filter With Complementary Features for Visual Tracking
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AI
DOI：10.1109/LSP.2020.3039933.png)
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摘要
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Different features describe different aspects of the object. Individually tailoring proper features for visual tracking is crucial to obtain high performance. In this letter, we propose a hybrid cascade filter to fuse handcrafted and deep features for exploiting their strengths. We complement the deep representation with handcrafted features to achieve better localization accuracy, as well as build a hybrid cascade structure using multiple observation models to achieve better robustness. Furthermore, a coarse-to-fine searching strategy is used for lowering the computational cost. Extensive experimental results on two benchmark datasets show that the proposed method performs favorably against the state-of-the-art trackers.
Keyword:
Observers
Visualization
Feature extraction
Robustness
Information filters
Target tracking
Signal processing algorithms
Deep features
hybrid cascade filter
handcrafted features
visual tracking
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
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