Feature mutual reinforcement learning and resampling for RGB-T tracking

Futian Wang; Xuan Zhou; Wenqi Wang

doi:10.1117/12.2674770

2 May 2023 Feature mutual reinforcement learning and resampling for RGB-T tracking

Futian Wang, Xuan Zhou, Wenqi Wang

Proceedings Volume 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023); 126422J (2023) https://doi.org/10.1117/12.2674770
Event: Second International Conference on Electronic Information Engineering, Big Data and Computer Technology (EIBDCT 2023), 2023, Xishuangbanna, China

Abstract

More and more researchers have paid attention to the tracking of visible-thermal infrared (RGB-T). How to fully exploit the complementary features of visible and thermal infrared images and fully integrate them is a key issue. After extracting image features, many researchers simply fuse the features by adding, connecting operations or designing fusion modules. However, these methods ignore the effects of different levels of fusion features on target modeling and specific feature extraction. In this work, we propose a RGB-T tracking network (MRLRNet) based on feature mutual reinforcement learning and resampling. Specifically, we design a feature mutual reinforcement learning module, which combines different layers of features to achieve progressive fusion. After each layer feature is extracted, the aggregation features are used to enhance specific modal features to achieve better specific feature representation and reduce noise and redundancy features. At the same time, we design a resampling module, which calculates the offset of two adjacent frames by phase correlation operation, and recalculates the Gaussian sample points to solve the problem of ground target loss caused by sudden camera movement. A large number of experiments on three RGB-T tracking datasets, GTOT, RGBT234 and LasHeR, demonstrate the effectiveness of this method.

Citation Download Citation

Futian Wang, Xuan Zhou, and Wenqi Wang "Feature mutual reinforcement learning and resampling for RGB-T tracking", Proc. SPIE 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023), 126422J (2 May 2023); https://doi.org/10.1117/12.2674770

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KEYWORDS

RGB color model

Feature fusion

Education and training

Feature extraction

Infrared radiation

Thermography

Visible radiation

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