Abstract
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).
Abstract (translated)
最近，关于人员重新识别（ReID）的研究兴趣越来越多地集中在基于视频的场景上，这些场景对于在多变和动态环境中进行稳健的监控和安全至关重要。然而，现有的基于视频的ReID方法往往忽视了从查询库对中的视频中选择最具区分性的特征以实现有效匹配的重要性。为了解决这个问题，我们提出了一种新颖的层次化和自适应生物识别专家混合（HAMoBE）框架，该框架利用预训练的大模型（如CLIP）提供的多层特征，并设计成模仿人类感知机制，独立建模关键生物识别特征——外观、静态身体形状以及动态步态——并自适应地整合这些特征。具体来说，HAMoBE包括两个层次：第一层级从冻结大模型提供的多层次表示中提取低级特征，而第二层级则包含专注于长期、短期和时序特征的专门专家。为了确保稳健匹配，我们引入了一种新的双输入决策门控网络，该网络可以根据每个专家与输入场景的相关性动态调整其贡献。在像MEVID这样的基准数据集上的广泛评估表明，我们的方法带来了显著的性能改进（例如，Rank-1准确率提高了+13.0%）。
URL
https://arxiv.org/abs/2508.05038
