Abstract
Video surveillance image analysis and processing is a challenging field in computer vision, with one of its most difficult tasks being Person Re-Identification (PRe-ID). PRe-ID aims to identify and track target individuals who have already been detected in a network of cameras, using a robust description of their pedestrian images. The success of recent research in person PRe-ID is largely due to effective feature extraction and representation, as well as the powerful learning of these features to reliably discriminate between pedestrian images. To this end, two powerful features, Convolutional Neural Networks (CNN) and Local Maximal Occurrence (LOMO), are modeled on multidimensional data using the proposed method, High-Dimensional Feature Fusion (HDFF). Specifically, a new tensor fusion scheme is introduced to leverage and combine these two types of features in a single tensor, even though their dimensions are not identical. To enhance the system's accuracy, we employ Tensor Cross-View Quadratic Analysis (TXQDA) for multilinear subspace learning, followed by cosine similarity for matching. TXQDA efficiently facilitates learning while reducing the high dimensionality inherent in high-order tensor data. The effectiveness of our approach is verified through experiments on three widely-used PRe-ID datasets: VIPeR, GRID, and PRID450S. Extensive experiments demonstrate that our approach outperforms recent state-of-the-art methods.
Abstract (translated)
视频监控图像分析和处理是计算机视觉领域中的一个挑战性课题，其中最难的任务之一就是行人再识别（Person Re-Identification，PRe-ID）。PRe-ID的目标是在由多个摄像头组成的网络中对已经检测到的特定个人进行识别与追踪。这需要通过强有力的描述来刻画行人的图像特征。近年来，PRe-ID研究的成功很大程度上归功于有效的特征提取和表示方法，以及这些特征的强大学习能力，使其能够可靠地区分行人图像。 为此，本文提出了一种高维特征融合（High-Dimensional Feature Fusion, HDFF）的方法，在这种方法中，利用两种强大的特征——卷积神经网络（Convolutional Neural Networks, CNN）和局部最大发生率（Local Maximal Occurrence, LOMO）来建模多维度数据。具体来说，我们引入了一种新的张量融合方案，即使这两种特征的维度不相同，也能将它们组合成一个单一的张量中进行处理。为了提高系统的准确性，我们在高阶张量数据固有的高维性学习过程中采用了张量跨视图二次分析（Tensor Cross-View Quadratic Analysis, TXQDA）和余弦相似度匹配的方法来进行多线性子空间学习。 我们通过在三个广泛使用的PRe-ID数据集：VIPeR、GRID和PRID450S上进行实验，验证了该方法的有效性。大量的实验证明我们的方法优于近期最先进的方法。
URL
https://arxiv.org/abs/2505.15825
