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Recognition of pedestrian trajectories and attributes with computer vision and deep learning techniques

机译：用计算机视觉和深层学习技术认识人行道轨迹和属性

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摘要

Analyzing the walking behavior of the public is vital for revealing the need for infrastructure design in a local neighborhood, supporting human-centric urban area development. Traditional walking behavior analysis practices relying on manual on-street surveys to collect pedestrian flow data are labor-intensive and tedious. On the contrary, automated video analytics using surveillance cameras based on computer vision and deep learning techniques appears more effective in generating pedestrian flow statistics. Nevertheless, most existing methods of pedestrian tracking and attribute recognition suffer from several challenging conditions, such as inter-person occlusion and appearance variations, which leads to ambiguous identities and hence inaccurate pedestrian flow statistics.Therefore, this paper proposes a more robust methodology of pedestrian tracking and attribute recognition, facilitating the analysis of pedestrian walking behavior. Specific limitations of a current state-of-the-art method are inferred, based on which several improvement strategies are proposed: 1) incorporating high-level pedestrian attributes to enhance pedestrian tracking, 2) a similarity measure integrating multiple cues for identity matching, and 3) a probation mechanism for more robust identity matching. From our evaluation using two public benchmark datasets, the developed strategies notably enhance the robustness of pedestrian tracking against the challenging conditions mentioned above. Subsequently, the outputs of trajectories and attributes are aggregated into fine-grained pedestrian flow statistics among different pedestrian groups. Overall, our developed framework can support a more comprehensive and reliable decision-making for human-centric planning and design in different urban areas. The framework is also applicable to exploiting pedestrian movement patterns in different scenes for analyses such as urban walkability evaluation. Moreover, the developed mechanisms are generalizable to future researches as a baseline, which provides generic insights of how to fundamentally enhance pedestrian tracking.
机译：分析公众的步行行为对于揭示当地社区的基础设施设计需要至关重要，支持以人为本的城市地区开发。传统的行走行为分析实践依赖于手工街头调查，以收集行人流量数据是劳动密集型和繁琐的。相反，根据计算机视觉和深度学习技术使用监控摄像机的自动视频分析在产生行人流动统计方面似乎更有效。尽管如此，最现有的行人跟踪和属性识别方法遭受了几种挑战性的条件，例如人际遮挡和外观变化，这导致了模糊的身份，因此不准确的行人流动统计数据。因此，本文提出了一种更强大的行人方法跟踪和属性识别，促进行人行走行为分析。推断出基于哪种改进策略的特定限制，基于提出了几种改进策略：1）包含高级步行属性以增强行人跟踪，2）相似度测量为身份匹配的多个提示集成了多个线索， 3）更强大的身份匹配的试用机制。通过使用两个公共基准数据集的评估，开发的策略显着提高了对上述具有挑战性的行人跟踪的鲁棒性。随后，轨迹和属性的输出聚集成不同行人组之间的细粒度的行人流统计。总体而言，我们发达的框架可以支持不同城市地区的人类规划和设计更全面可靠的决策。该框架也适用于利用不同场景的人行动模式，以进行城市可​​行性评估等分析。此外，发达的机制是概遍的，将来的研究是基线，这提供了如何从根本上增强行人跟踪的通用洞察。

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  • 来源
    《Advanced engineering informatics》 |2021年第8期|101356.1-101356.18|共18页
  • 作者

    Peter Kok-Yiu Wong; Han Luo; Mingzhu Wang; Pak Him Leung; Jack C.P. Cheng;

  • 作者单位

    Department of Civil and Environmental Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong China;

    Department of Civil and Environmental Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong China;

    School of Architecture Building and Civil Engineering Loughborough University Loughborough United Kingdom;

    Department of Civil and Environmental Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong China;

    Department of Civil and Environmental Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong China;

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  • 正文语种 eng
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  • 关键词

    Computer vision; Deep learning; Pedestrian attribute recognition; Pedestrian trajectory tracking; Walking behavior analysis;

    机译：计算机视觉;深度学习;行人属性识别;行人轨迹跟踪;行走行为分析;

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