Abstract
Person Re-Identification (Re-ID) is a very important task in video surveillance systems such as tracking people, finding people in public places, or analysing customer behavior in supermarkets. Although there have been many works to solve this problem, there are still remaining challenges such as large-scale datasets, imbalanced data, viewpoint, fine grained data (attributes), the Local Features are not employed at semantic level in online stage of Re-ID task, furthermore, the imbalanced data problem of attributes are not taken into consideration. This paper has proposed a Unified Re-ID system consisted of three main modules such as Pedestrian Attribute Ontology (PAO), Local Multi-task DCNN (Local MDCNN), Imbalance Data Solver (IDS). The new main point of our Re-ID system is the power of mutual support of PAO, Local MDCNN and IDS to exploit the inner-group correlations of attributes and pre-filter the mismatch candidates from Gallery set based on semantic information as Fashion Attributes and Facial Attributes, to solve the imbalanced data of attributes without adjusting network architecture and data augmentation. We experimented on the well-known Market1501 dataset. The experimental results have shown the effectiveness of our Re-ID system and it could achieve the higher performance on Market1501 dataset in comparison to some state-of-the-art Re-ID methods.
Abstract (translated)
人重新识别（Re-ID）是视频监控系统中的一个非常重要的任务，例如跟踪人员、在公共场所寻找人员或分析超市中顾客的行为。尽管已经有许多研究致力于解决这一问题，但仍存在一些挑战，如大规模数据集的处理、数据不平衡、视角变化以及细粒度数据（属性）等问题。此外，在人重新识别任务的在线阶段，局部特征并未被用于语义层面的应用，同时，属性的数据不平衡问题也未得到充分考虑。 本文提出了一种统一的人重新识别系统，该系统由三个主要模块组成：行人属性本体论（PAO）、局部多任务深度卷积神经网络（Local MDCNN）和数据不平衡求解器（IDS）。我们的人重新识别系统的创新点在于，这三个模块——即PAO、Local MDCNN 和 IDS 能够相互支持，通过利用属性之间的内部分组相关性，并基于语义信息如时尚属性和面部属性对Gallery集合中的不匹配候选者进行预筛选，以解决属性数据不平衡问题而不调整网络架构或数据增强。 我们在著名的Market1501数据集上进行了实验。实验结果表明了我们的人重新识别系统的效果显著，在Market1501数据集上的性能优于一些最新的Re-ID方法。
URL
https://arxiv.org/abs/2506.04143
