Location-Aware and Regularization-Adaptive Correlation Filters for Robust Visual Tracking

Correlation filter (CF) has recently been widely used for visual tracking. The estimation of the search window and the filter-learning strategies is the key component of the CF trackers. Nevertheless, prevalent CF models separately address these issues in heuristic manners. The commonly used CF mode...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 32; no. 6; pp. 2430 - 2442
Main Authors: Liu, Risheng, Chen, Qianru, Yao, Yuansheng, Fan, Xin, Luo, Zhongxuan
Format: Journal Article
Language:English
Published: United States IEEE 01.06.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Artificial Intelligence
Bilevel learning
Correlation
correlation filter (CF)
Humans
Image Processing, Computer-Assisted
Learning
location-aware
Machine Learning
Models, Neurological
Neural Networks, Computer
Optical tracking
Optimization
Pattern Recognition, Automated
Problem solving
Psychomotor Performance
Regularization
regularization-adaptive
Robustness
Searching
Target tracking
Task analysis
visual tracking
Visualization
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary:Correlation filter (CF) has recently been widely used for visual tracking. The estimation of the search window and the filter-learning strategies is the key component of the CF trackers. Nevertheless, prevalent CF models separately address these issues in heuristic manners. The commonly used CF models directly set the estimated location in the previous frame as the search center for the current one. Moreover, these models usually rely on simple and fixed regularization for filter learning, and thus, their performance is compromised by the search window size and optimization heuristics. To break these limits, this article proposes a location-aware and regularization-adaptive CF (LRCF) for robust visual tracking. LRCF establishes a novel bilevel optimization model to address simultaneously the location-estimation and filter-training problems. We prove that our bilevel formulation can successfully obtain a globally converged CF and the corresponding object location in a collaborative manner. Moreover, based on the LRCF framework, we design two trackers named LRCF-S and LRCF-SA and a series of comparisons to prove the flexibility and effectiveness of the LRCF framework. Extensive experiments on different challenging benchmark data sets demonstrate that our LRCF trackers perform favorably against the state-of-the-art methods in practice.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2020.3005447