利用路径图像涂抹和三维点云特征进行保护隐私的行人追踪

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Masakazu Ohno, Riki Ukyo, Tatsuya Amano, Hamada Rizk, Hirozumi Yamaguchi
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引用次数: 0

摘要

跟踪大型公共区域的人流至关重要,但确保隐私也是头等大事。传统的视觉跟踪系统可能会获取持久和永久的身份识别信息,从而危及个人身份,这引起了人们的关注。此外,在厕所附近等区域,任何形式的捕捉人类行为的数据采集都应避免,因此适当解决和补充这些盲点以全面分析整个区域的行人流动情况也至关重要。在本文中,我们介绍了使用分布式三维激光雷达(光探测与测距)的行人跟踪算法,该算法以三维点云的形式捕捉行人,省略了可识别的特征。我们的系统利用历史运动数据和三维点云特征,辅以生成扩散模型来预测未见区域的轨迹,从而弥补了盲点。在一个有 70 个激光雷达的大型测试平台上,该系统的 F 测量值达到了 0.98,凸显了其作为领先的隐私保护跟踪解决方案的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Privacy-preserving pedestrian tracking with path image inpainting and 3D point cloud features

Tracking pedestrian flow in large public areas is vital, yet ensuring privacy is paramount. Traditional visual-based tracking systems are raising concerns for potentially obtaining persistent and permanent identifiers that can compromise individual identities. Moreover, in areas such as the vicinity of restrooms, any form of data acquisition capturing human behavior should be refrained from, making it also crucial to appropriately address and complement these blind spots for a comprehensive analysis of pedestrian movement in the entire area. In this paper, we present our pedestrian tracking algorithm using distributed 3D LiDARs (Light Detection and Ranging), which capture pedestrians as 3D point clouds, omitting identifiable features. Our system bridges blind spots by leveraging historical movement data and 3D point cloud features, complemented by a generative diffusion model to predict trajectories in unseen areas. In a large-scale testbed with 70 LiDARs, the system achieved a 0.98 F-measure, highlighting its potential as a leading privacy-preserving tracking solution.

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来源期刊
Pervasive and Mobile Computing
Pervasive and Mobile Computing COMPUTER SCIENCE, INFORMATION SYSTEMS-TELECOMMUNICATIONS
CiteScore
7.70
自引率
2.30%
发文量
80
审稿时长
68 days
期刊介绍: As envisioned by Mark Weiser as early as 1991, pervasive computing systems and services have truly become integral parts of our daily lives. Tremendous developments in a multitude of technologies ranging from personalized and embedded smart devices (e.g., smartphones, sensors, wearables, IoTs, etc.) to ubiquitous connectivity, via a variety of wireless mobile communications and cognitive networking infrastructures, to advanced computing techniques (including edge, fog and cloud) and user-friendly middleware services and platforms have significantly contributed to the unprecedented advances in pervasive and mobile computing. Cutting-edge applications and paradigms have evolved, such as cyber-physical systems and smart environments (e.g., smart city, smart energy, smart transportation, smart healthcare, etc.) that also involve human in the loop through social interactions and participatory and/or mobile crowd sensing, for example. The goal of pervasive computing systems is to improve human experience and quality of life, without explicit awareness of the underlying communications and computing technologies. The Pervasive and Mobile Computing Journal (PMC) is a high-impact, peer-reviewed technical journal that publishes high-quality scientific articles spanning theory and practice, and covering all aspects of pervasive and mobile computing and systems.
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