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Unit Correlation With Interactive Feature for Robust and Effective Tracking
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AI
DOI：10.1109/TMM.2023.3321476.png)
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Abstract
En 中文
For robust and effective tracking, most efforts strive to design a powerful representation target model, while we are inspired by the idea of knowing oneself and knowing others to major in both the target and non-target features. In this work, we propose a unit correlation with interactive feature tracker (UCIF), which utilizes feature interaction and independent correlation operation to improve robustness and effectiveness. Specifically, we first propose a feature integration network, in which the feature enhancement module concentrates on enhancing the tracker's representation ability for both target and non-target. The feature interaction module is in charge of completing the interactive learning between target and non-target features. Then, considering the potential risk of blurring spatial information in regular correlation operation, a unit correlation network is presented, where the convolution sampling strategy can integrate the target features as well as reduce the computation costs. The unit kernel for correlation operation can protect the target spatial information. The channel ranking module suppresses background interference via weight assignment. Extensive experiments are conducted on both the short-term and long-term challenging benchmarks, including OTB2015, NFS, UAV123, TrackingNet, GOT-10 k, TLP, LaSOT and VOT-LT2019. Our tracker achieves remarkable performance in robustness and effectiveness.
Keywords:
Target tracking
Correlation
Feature extraction
Robustness
Transformers
Visualization
Finite element analysis
Visual object tracking
feature enhancement
feature interaction
convolution sampling
unit correlation
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