分层自适应融合生物特征，提升视频行人重识别准确率
HAMoBE: Hierarchical and Adaptive Mixture of Biometric Experts for Video-based Person ReID
- 分三层提取外观、体型、步态特征并自适应融合
- 在MEVID数据集上提升13.0%的Rank-1准确率
- 适合需要高精度视频行人识别的应用场景
近期研究关注视频行人重识别（ReID），这对复杂动态环境下的安防系统至关重要。然而，现有方法常忽视从查询与候选视频对中选取最具区分性的特征。为此，我们提出分层自适应生物专家混合框架（HAMoBE），利用预训练大模型（如CLIP）的多层特征，模仿人类感知机制，独立建模外观、静态体型和动态步态等关键生物特征，并自适应融合。HAMoBE包含两层：第一层从冻结大模型的多层表示中提取低层特征；第二层由专注于长期、短期及时间特征的专用专家组成。为保证匹配鲁棒性，引入双输入决策门控网络，根据输入场景动态调整各专家贡献。在MEVID等基准上的大量实验表明，该方法显著提升性能（例如，Rank-1准确率提升13.0%）。
原文摘要 · Abstract (English)
Recently, research interest in person re-identification (ReID) has increasingly focused on video-based scenarios, which are essential for robust surveillance and security in varied and dynamic environments. However, existing video-based ReID methods often overlook the necessity of identifying and selecting the most discriminative features from both videos in a query-gallery pair for effective matching. To address this issue, we propose a novel Hierarchical and Adaptive Mixture of Biometric Experts (HAMoBE) framework, which leverages multi-layer features from a pre-trained large model (e.g., CLIP) and is designed to mimic human perceptual mechanisms by independently modeling key biometric features--appearance, static body shape, and dynamic gait--and adaptively integrating them. Specifically, HAMoBE includes two levels: the first level extracts low-level features from multi-layer representations provided by the frozen large model, while the second level consists of specialized experts focusing on long-term, short-term, and temporal features. To ensure robust matching, we introduce a new dual-input decision gating network that dynamically adjusts the contributions of each expert based on their relevance to the input scenarios. Extensive evaluations on benchmarks like MEVID demonstrate that our approach yields significant performance improvements (e.g., +13.0% Rank-1 accuracy).
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。