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Recognition of pedestrian trajectories and attributes with computer vision and deep learning techniques
Advanced Engineering Informatics ( IF 8.8 ) Pub Date : 2021-07-13 , DOI: 10.1016/j.aei.2021.101356
Peter Kok-Yiu Wong 1 , Han Luo 1 , Mingzhu Wang 2 , Pak Him Leung 1 , Jack C.P. Cheng 1
Affiliation  

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.



中文翻译:

使用计算机视觉和深度学习技术识别行人轨迹和属性

分析公众的步行行为对于揭示当地社区基础设施设计的需求、支持以人为本的城市地区发展至关重要。传统的步行行为分析实践依赖手动街上调查来收集行人流量数据,劳动密集型且繁琐。相反,使用基于计算机视觉和深度学习技术的监控摄像头的自动视频分析在生成行人流量统计数据方面似乎更有效。然而,大多数现有的行人跟踪和属性识别方法都存在一些具有挑战性的条件,例如人际遮挡和外观变化,这会导致身份不明确,从而导致行人流量统计不准确。

因此,本文提出了一种更稳健的行人跟踪和属性识别方法,有助于分析行人步行行为。推断当前最先进方法的具体局限性,在此基础上提出了几种改进策略:1) 结合高级行人属性以增强行人跟踪,2) 集成多个线索进行身份匹配的相似性度量,和 3) 更强大的身份匹配的试用机制。从我们使用两个公共基准数据集进行的评估来看,所开发的策略显着增强了行人跟踪对上述挑战性条件的鲁棒性。随后,轨迹和属性的输出被聚合成不同行人群体之间的细粒度行人流统计。总体而言,我们开发的框架可以为不同城市地区以人为本的规划和设计提供更全面、更可靠的决策。该框架还适用于利用不同场景中的行人运动模式进行分析,例如城市步行性评估。此外,所开发的机制可作为基线推广到未来的研究中,这为如何从根本上增强行人跟踪提供了通用见解。

更新日期:2021-07-13
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