MFAPCE algorithm uses deep features and color features to illustrate the appearance of the target. And the average peak correlation energy is used to detect the confidence of the response and to determine whether to update the model; and the algorithm uses context information to constrain the background information.
Feb 19, 2019
Feb 1, 2019 · The MFAPCE tracking algorithm combines deep features with color features and uses average peak correlation energy to measure confidence level.
This paper proposes the Multiple Features and Average Peak Correlation Energy (MFAPCE) tracking algorithm, MFAPCE tracking algorithm combines deep features with ...
Correlation filter tracking algorithm based on multiple features and average peak correlation energy. ... Peak Correlation Energy (MFAPCE) tracking algorithm ...
The correlation filter is used to localize the target at a high speed via the maximum response value. Two indicators of maximum response value and average peak- ...
Jun 17, 2022 · Sun et al. combine color and depth features and propose an average peak correlation energy tracking algorithm.
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[PDF] Visual Object Tracking using Adaptive Correlation Filters
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Although not commonly used, correlation filters can track complex objects through rotations, occlusions and other distractions at over 20 times the rate of ...
May 7, 2024 · In the framework of the conventional correlation filtering algorithm, the algorithm will update the tracking model after each frame tracking to ...
Apr 5, 2023 · a background learning correlation filtering algorithm based on multi-feature fusion is proposed. ... average peak-to-correlation energy ...
Firstly, we normalize the response peaks of different features to dynamically assign feature weights for the purpose of combining HOG features and color ...
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