@Article{cmc.2026.077977,
AUTHOR = {Wanhui Qiao, Xiaorui Zhang, Wei Sun, Shiyu Zhou, Kaibo Wang},
TITLE = {Dual-Stream Feature Decoupling and Temporal Variational Bayesian Inference for Ship Re-Identification with Incomplete Data},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/cmc/online/detail/26386},
ISSN = {1546-2226},
ABSTRACT = {Ship re-identification (Re-ID) aims to match ship identities across disjoint camera views and separated time periods, which is critical for maritime target tracking and law enforcement. In real-world surveillance, variations in target distance and viewing angle frequently produce partial views and occlusions, leading to missing geometric components and fragmented appearance cues. Such incomplete observations substantially degrade the robustness and generalization of conventional single-frame methods that rely on global appearance representations. To address these challenges, this study proposes a new ship re-identification framework based on dual-stream feature decoupling and temporal variational Bayesian inference. The proposed method explicitly disentangles ship representations into appearance and structural streams, and leverages multi-frame temporal context to infer missing components and enhance discriminability under partial visibility. Specifically, a ResNet-based splitter trained adversarially against two discriminators is employed to decouple the input representation into separate feature streams. The decoupled streams are then modeled over time using a bidirectional LSTM (BiLSTM) together with a visibility-probability estimator. A graph-structured spatial prior, parameterized via a graph attention network (GAT), serves as the variational prior. Given sequential observations, the variational inference module estimates posterior distributions for missing components and performs probabilistic completion in the latent space. The framework is trained end-to-end using cross-entropy and triplet losses. Extensive experiments on the Ship-CH dataset demonstrate that our method achieves 85.67% mAP and 93.67% Rank-1 accuracy, exhibiting superior robustness under occlusion and partial visibility.},
DOI = {10.32604/cmc.2026.077977}
}