Learning discriminative local contexts for person re-identification in vehicle surveillance scenarios

IF 3.7 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pattern Analysis and Applications Pub Date : 2024-02-28 DOI:10.1007/s10044-024-01219-6

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引用次数: 0

Abstract

In recent years, person re-identification (Re-ID) has been widely used in intelligent surveillance and security. However, Re-ID faces many challenges in the vehicle surveillance scenario, such as heavy occlusion, misalignment, and similar appearances. Most Re-ID methods focus on learning discriminative global features or dividing regions for local feature learning, which may ignore critical but subtle differences between pedestrians. In this paper, we propose a local context aggregation branch for learning discriminative local contexts at multiple scales, which can supplement the critical detailed information omitted in global features. Specifically, we exploit dilated convolutions to simulate spatial feature pyramid to capture multi-scale spatial contexts efficiently. The essential information that can distinguish different pedestrians is then emphasized. Besides, we construct a Re-ID dataset named BSV for vehicle surveillance scenarios and propose a triplet loss with station constraint enhancement, which utilizes additional valuable station information to construct penalty terms to improve the performance of Re-ID further. Extensive experiments are conducted on the proposed BSV dataset and two standard Re-ID datasets, and the results validate the effectiveness of our method.

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为车辆监控场景中的人员再识别学习辨别性局部语境

摘要近年来，人员再识别（Re-ID）被广泛应用于智能监控和安防领域。然而，在车辆监控场景中，重新识别面临着许多挑战，如严重遮挡、错位和相似外观等。大多数 Re-ID 方法都侧重于学习具有区分性的全局特征或划分区域进行局部特征学习，这可能会忽略行人之间关键但细微的差异。在本文中，我们提出了一个局部上下文聚合分支，用于学习多尺度的辨别性局部上下文，从而补充全局特征中遗漏的关键细节信息。具体来说，我们利用扩张卷积来模拟空间特征金字塔，从而有效捕捉多尺度空间上下文。这样就能突出区分不同行人的基本信息。此外，我们还构建了一个名为 BSV 的 Re-ID 数据集，用于车辆监控场景，并提出了一种带有站点约束增强的三重损失法，利用额外的有价值站点信息来构建惩罚项，从而进一步提高 Re-ID 的性能。我们在提出的 BSV 数据集和两个标准 Re-ID 数据集上进行了广泛的实验，结果验证了我们方法的有效性。

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来源期刊

Pattern Analysis and Applications 工程技术-计算机：人工智能

CiteScore

7.40

自引率

2.60%

发文量

审稿时长

13.5 months

期刊介绍： The journal publishes high quality articles in areas of fundamental research in intelligent pattern analysis and applications in computer science and engineering. It aims to provide a forum for original research which describes novel pattern analysis techniques and industrial applications of the current technology. In addition, the journal will also publish articles on pattern analysis applications in medical imaging. The journal solicits articles that detail new technology and methods for pattern recognition and analysis in applied domains including, but not limited to, computer vision and image processing, speech analysis, robotics, multimedia, document analysis, character recognition, knowledge engineering for pattern recognition, fractal analysis, and intelligent control. The journal publishes articles on the use of advanced pattern recognition and analysis methods including statistical techniques, neural networks, genetic algorithms, fuzzy pattern recognition, machine learning, and hardware implementations which are either relevant to the development of pattern analysis as a research area or detail novel pattern analysis applications. Papers proposing new classifier systems or their development, pattern analysis systems for real-time applications, fuzzy and temporal pattern recognition and uncertainty management in applied pattern recognition are particularly solicited.