Recent advancement of research in biometrics, computer vision, and natural language processing has discovered opportunities for person retrieval from surveillance videos using textual query. The prime objective of a surveillance system is to locate a person using a description, e.g., a short woman with a pink t-shirt and white skirt carrying a black purse. She has brown hair. Such a description contains attributes like gender, height, type of clothing, colour of clothing, hair colour, and accessories. Such attributes are formally known as soft biometrics. They help bridge the semantic gap between a human description and a machine as a textual query contains the person's soft biometric attributes. It is also not feasible to manually search through huge volumes of surveillance footage to retrieve a specific person. Hence, automatic person retrieval using vision and language-based algorithms is becoming popular. In comparison to other state-of-the-art reviews, the contribution of the paper is as follows: 1. Recommends most discriminative soft biometrics for specifiic challenging conditions. 2. Integrates benchmark datasets and retrieval methods for objective performance evaluation. 3. A complete snapshot of techniques based on features, classifiers, number of soft biometric attributes, type of the deep neural networks, and performance measures. 4. The comprehensive coverage of person retrieval from handcrafted features based methods to end-to-end approaches based on natural language description.

中文翻译：

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使用文本查询进行监视中的人员检索：回顾

生物识别，计算机视觉和自然语言处理方面的最新研究进展为使用文本查询从监视视频中检索人员提供了机会。监视系统的主要目的是使用描述来查找人员，例如，一个身穿粉红色T恤，白色裙子和黑色钱包的矮个女人。她有棕色的头发。这样的描述包含诸如性别，身高，衣服类型，衣服颜色，头发颜色和配饰之类的属性。这些属性在形式上被称为软生物统计。它们帮助弥合人类描述和机器之间的语义鸿沟，因为文本查询包含该人的软生物特征。手动搜索大量监视录像以检索特定人员也是不可行的。因此，使用视觉和基于语言的算法进行自动人员检索正变得越来越流行。与其他最新的评论相比，本文的贡献如下：1.对于特定的挑战性条件，建议采用最具区分性的软生物识别技术。2.集成基准数据集和检索方法以进行客观绩效评估。3.基于功能，分类器，软生物识别属性的数量，深度神经网络的类型和性能指标的技术的完整快照。4.从基于手工特征的方法到基于自然语言描述的端到端方法的人员检索的全面覆盖。对于特定的挑战性条件，建议使用最具区分性的软生物识别技术。2.集成基准数据集和检索方法以进行客观绩效评估。3.基于功能，分类器，软生物识别属性的数量，深度神经网络的类型和性能指标的技术的完整快照。4.从基于手工特征的方法到基于自然语言描述的端到端方法的人员检索的全面覆盖。对于特定的挑战性条件，建议使用最具区分性的软生物识别技术。2.集成基准数据集和检索方法以进行客观绩效评估。3.基于功能，分类器，软生物识别属性的数量，深度神经网络的类型和性能指标的技术的完整快照。4.从基于手工特征的方法到基于自然语言描述的端到端方法的人员检索的全面覆盖。