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。

发挥轨迹片段在无监督视频人物再辨识中的潜力

Unleashing the Potential of Tracklets for Unsupervised Video Person  Re-Identification

Accurate detection and tracking of objects is vital for effective video
understanding. In previous work, the two tasks have been combined in a way that
tracking is based heavily on detection, but the detection benefits marginally
from the tracking. To increase synergy, we propose to more tightly integrate
the tasks by conditioning the object detection in the current frame on
tracklets computed in prior frames. With this approach, the object detection
results not only have high detection responses, but also improved coherence
with the existing tracklets. This greater coherence leads to estimated object
trajectories that are smoother and more stable than the jittered paths obtained
without tracklet-conditioned detection. Over extensive experiments, this
approach is shown to achieve state-of-the-art performance in terms of both
detection and tracking accuracy, as well as noticeable improvements in tracking
stability.

本文提出一种新方法，通过在先前帧中计算的轨迹片段上使用对象检测来增加检测与追踪之间的协作，以获得更平稳和稳定的对象轨迹，从而在检测和跟踪精度上实现最先进的表现。

基于轨迹的物体检测和追踪集成

Integrated Object Detection and Tracking with Tracklet-Conditioned  Detection

Person re-identification is critical in surveillance applications. Current
approaches rely on appearance based features extracted from a single or
multiple shots of the target and candidate matches. These approaches are at a
disadvantage when trying to distinguish between candidates dressed in similar
colors or when targets change their clothing. In this paper we propose a
dynamics-based feature to overcome this limitation. The main idea is to capture
soft biometrics from gait and motion patterns by gathering dense short
trajectories (tracklets) which are Fisher vector encoded. To illustrate the
merits of the proposed features we introduce three new "appearance-impaired"
datasets. Our experiments on the original and the appearance impaired datasets
demonstrate the benefits of incorporating dynamics-based information with
appearance-based information to re-identification algorithms.

本文提出了基于动力学特征的人物再识别方法，包括采集人体运动轨迹（tracklets）并进行 Fisher 向量编码等方法，通过实验证明了该方法在监控应用中的优越性。