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학술대회 REET: Region-Enhanced Transformer for Person Re-Identification
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이경오, 김광주, 장인수, 김병근
International Conference on Advanced Video and Signal-based Surveillance (AVSS) 2022, pp.1-8
22ZD1100, 대경권 지역산업 기반 ICT 융합기술 고도화 지원사업, 문기영
Person re-identification (ReID) plays a significant role in intelligent surveillance systems. However, it is challenging due to large variations in the intra-class, where the same person is captured in different scenes or cameras. The current person ReID research focuses on creating robust features for class distinction and generalizing neural networks for covering various target domains to address the issue. Recently, after the achievement of vision transformers, the application of transformers has also begun to person ReID studies. The transformer-based methods have improved quantitative performance of person ReID; however, they still suffer from class distinction. Therefore, this paper proposes a novel region-enhanced transformer (REET) to create robust ReID features. Unlike conventional transformer-based approaches, the REET emphasizes the tokens generated by region-level. Our method achieves state-of-the-art results on three public datasets; Market1501, DukeMTMC, and CUHK-03.
KSP 제안 키워드
Intelligent Surveillance, Person Re-Identification, Public Datasets, Robust feature, Surveillance system, neural network, region-level, state-of-The-Art, transformer-based