With rich temporal-spatial information, video-based person re-identification
methods have shown broad prospects. Although tracklets can be easily obtained
with ready-made tracking models, annotating identities is still expensive and
impractical. Therefore, some video-based methods propose using only a few
identity annotations or camera labels to facilitate feature learning. They also
simply average the frame features of each tracklet, overlooking unexpected
variations and inherent identity consistency within tracklets. In this paper,
we propose the Self-Supervised Refined Clustering (SSR-C) framework without
relying on any annotation or auxiliary information to promote unsupervised
video person re-identification. Specifically, we first propose the
Noise-Filtered Tracklet Partition (NFTP) module to reduce the feature bias of
tracklets caused by noisy tracking results, and sequentially partition the
noise-filtered tracklets into "sub-tracklets". Then, we cluster and further
merge sub-tracklets using the self-supervised signal from tracklet partition,
which is enhanced through a progressive strategy to generate reliable pseudo
labels, facilitating intra-class cross-tracklet aggregation. Moreover, we
propose the Class Smoothing Classification (CSC) loss to efficiently promote
model learning. Extensive experiments on the MARS and DukeMTMC-VideoReID
datasets demonstrate that our proposed SSR-C for unsupervised video person
re-identification achieves state-of-the-art results and is comparable to
advanced supervised methods.

本文提出了一种无监督视频人员重识别的自监督精细聚类 (SSR-C) 框架，通过减少由于嘈杂跟踪结果引起的 tracklets 特征偏差的噪声过滤模块 (NFTP)，将 tracklets 划分为 “子 tracklets”，利用自监督信号进行聚类并进一步合并子 tracklets 以生成可靠的伪标签，从而促进类内跨 tracklet 聚合；此外，通过效率提升模型学习的 Class Smoothing Classification (CSC) 损失函数。在 MARS 和 DukeMTMC-VideoReID 数据集上的广泛实验验证了我们提出的 SSR-C 方法在无监督视频人员重识别领域取得了最新成果，并与先进的有监督方法相媲美。