RGBT Tracking via Challenge-Based Appearance Disentanglement and Interaction

RGB and thermal source data suffer from both shared and specific challenges, and how to explore and exploit them plays a critical role in representing the target appearance in RGBT tracking. In this paper, we propose a novel approach, which performs target appearance representation disentanglement a...

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Vydáno v:IEEE transactions on image processing Ročník 33; s. 1753 - 1767
Hlavní autoři: Liu, Lei, Li, Chenglong, Xiao, Yun, Ruan, Rui, Fan, Minghao
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Adaptation models
aggregation interaction
challenge-based appearance disentanglement
Data models
Feature extraction
guidance interaction
Lighting
Mathematical models
Modules
Parameters
Representations
RGBT tracking
Target tracking
Tracking
Training
Training data
training data generation
ISSN:1057-7149, 1941-0042, 1941-0042
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Shrnutí:RGB and thermal source data suffer from both shared and specific challenges, and how to explore and exploit them plays a critical role in representing the target appearance in RGBT tracking. In this paper, we propose a novel approach, which performs target appearance representation disentanglement and interaction via both modality-shared and modality-specific challenge attributes, for robust RGBT tracking. In particular, we disentangle the target appearance representations via five challenge-based branches with different structures according to their properties, including three parameter-shared branches to model modality-shared challenges and two parameter-independent branches to model modality-specific challenges. Considering the complementary advantages between modality-specific cues, we propose a guidance interaction module to transfer discriminative features from one modality to another one to enhance the discriminative ability of weak modality. Moreover, we design an aggregation interaction module to combine all challenge-based target representations, which could form more discriminative target representations and fit the challenge-agnostic tracking process. These challenge-based branches are able to model the target appearance under certain challenges so that the target representations can be learned by a few parameters even in the situation of insufficient training data. In addition, to relieve labor costs and avoid label ambiguity, we design a generation strategy to generate training data with different challenge attributes. Comprehensive experiments demonstrate the superiority of the proposed tracker against the state-of-the-art methods on four benchmark datasets.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2024.3371355