HUM-CARD: A human crowded annotated real dataset

IF 3 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems Pub Date : 2024-05-21 DOI:10.1016/j.is.2024.102409

Giovanni Di Gennaro , Claudia Greco , Amedeo Buonanno , Marialucia Cuciniello , Terry Amorese , Maria Santina Ler , Gennaro Cordasco , Francesco A.N. Palmieri , Anna Esposito

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引用次数: 0

Abstract

The growth of data-driven approaches typical of Machine Learning leads to an ever-increasing need for large quantities of labeled data. Unfortunately, these attributions are often made automatically and/or crudely, thus destroying the very concept of “ground truth” they are supposed to represent. To address this problem, we introduce HUM-CARD, a dataset of human trajectories in crowded contexts manually annotated by nine experts in engineering and psychology, totaling approximately $5000$ hours. Our multidisciplinary labeling process has enabled the creation of a well-structured ontology, accounting for both individual and contextual factors influencing human movement dynamics in shared environments. Preliminary and descriptive analyzes are presented, highlighting the potential benefits of this dataset and its methodology in various research challenges.

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HUM-CARD：人类人群注释真实数据集

机器学习中典型的数据驱动方法的发展导致对大量标注数据的需求与日俱增。遗憾的是，这些归因往往是自动和/或粗略地进行的，从而破坏了它们本应代表的 "基本事实 "的概念。为了解决这个问题，我们推出了 HUM-CARD，这是一个由九位工程学和心理学专家人工标注的数据集，包含了人类在拥挤环境中的活动轨迹，总时长约 5000 小时。我们的多学科标注过程创建了一个结构合理的本体，既考虑了影响人类在共享环境中运动动态的个体因素，也考虑了环境因素。本文介绍了初步的描述性分析，强调了该数据集及其方法在各种研究挑战中的潜在优势。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Information Systems 工程技术-计算机：信息系统

CiteScore

9.40

自引率

2.70%

发文量

112

审稿时长

53 days

期刊介绍： Information systems are the software and hardware systems that support data-intensive applications. The journal Information Systems publishes articles concerning the design and implementation of languages, data models, process models, algorithms, software and hardware for information systems. Subject areas include data management issues as presented in the principal international database conferences (e.g., ACM SIGMOD/PODS, VLDB, ICDE and ICDT/EDBT) as well as data-related issues from the fields of data mining/machine learning, information retrieval coordinated with structured data, internet and cloud data management, business process management, web semantics, visual and audio information systems, scientific computing, and data science. Implementation papers having to do with massively parallel data management, fault tolerance in practice, and special purpose hardware for data-intensive systems are also welcome. Manuscripts from application domains, such as urban informatics, social and natural science, and Internet of Things, are also welcome. All papers should highlight innovative solutions to data management problems such as new data models, performance enhancements, and show how those innovations contribute to the goals of the application.