Apostolakis Konstantinos C, Dimitriou Nikolaos, Margetis George, Ntoa Stavroula, Tzovaras Dimitrios, Stephanidis Constantine
Institute of Computer Science, Foundation for Research and Technology Hellas, Heraklion, Crete, GR-70013, Greece.
Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, GR-57001, Greece.
Open Res Eur. 2022 Jan 21;1:87. doi: 10.12688/openreseurope.13715.2. eCollection 2021.
Augmented reality (AR) and artificial intelligence (AI) are highly disruptive technologies that have revolutionised practices in a wide range of domains, including the security sector. Several law enforcement agencies (LEAs) employ AI in their daily operations for forensics and surveillance. AR is also gaining traction in security, particularly with the advent of affordable wearable devices. Equipping police officers with the tools to facilitate an elevated situational awareness (SA) in patrolling and tactical scenarios is expected to improve LEAs' safety and capacity to deliver crucial blows against terrorist and/or criminal threats. In this paper we present DARLENE, an ecosystem incorporating novel AI techniques for activity recognition and pose estimation tasks, combined with a wearable AR framework for visualization of the inferenced results via dynamic content adaptation according to the wearer's stress level and operational context. The concept has been validated with end-users through co-creation workshops, while the decision-making mechanism for enhancing LEAs' SA has been assessed with experts. Regarding computer vision components, preliminary tests of the instance segmentation method for humans' and objects' detection have been conducted on a subset of videos from the RWF-2000 dataset for violence detection, which have also been used to test a human pose estimation method that has so far exhibited impressive results, constituting the basis of further developments in DARLENE. Evaluation results highlight that target users are positive towards the adoption of the proposed solution in field operations, and that the SA decision-making mechanism produces highly acceptable outcomes. Evaluation of the computer vision components yielded promising results and identified opportunities for improvement. This work provides the context of the DARLENE ecosystem and presents the DARLENE architecture, analyses its individual technologies, and demonstrates preliminary results, which are positive both in terms of technological achievements and user acceptance of the proposed solution.
增强现实（AR）和人工智能（AI）是极具颠覆性的技术，它们已经彻底改变了包括安全领域在内的广泛领域的实践方式。一些执法机构（LEA）在日常运营中使用人工智能进行法医鉴定和监视。随着价格亲民的可穿戴设备的出现，AR在安全领域也越来越受到关注。为警察配备工具，以便在巡逻和战术场景中提高态势感知（SA）能力，有望提高执法机构的安全性，并增强其打击恐怖主义和/或犯罪威胁的能力。在本文中，我们介绍了DARLENE，这是一个生态系统，它结合了用于活动识别和姿势估计任务的新型人工智能技术，并结合了一个可穿戴AR框架，用于根据佩戴者的压力水平和操作环境通过动态内容适配来可视化推理结果。该概念已通过共创研讨会与最终用户进行了验证，同时还与专家一起评估了增强执法机构态势感知的决策机制。关于计算机视觉组件，已经对来自RWF-2000暴力检测数据集的一部分视频进行了人体和物体检测实例分割方法的初步测试，这些视频也被用于测试一种人体姿势估计方法，该方法迄今为止已取得了令人印象深刻的结果，构成了DARLENE进一步发展的基础。评估结果表明，目标用户对在现场行动中采用所提出的解决方案持积极态度，并且态势感知决策机制产生了高度可接受的结果。对计算机视觉组件的评估产生了有希望的结果，并确定了改进的机会。这项工作提供了DARLENE生态系统的背景，介绍了DARLENE架构，分析了其各个技术，并展示了初步结果，这些结果在技术成就和用户对所提出解决方案的接受度方面都是积极的。
