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Application-driven merging and analysis of person trajectories for distributed smart camera networks

机译：应用驱动的分布式智能相机网络人员轨迹的合并和分析

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摘要

Tracking of persons and analysis of their trajectories are important tasks of surveillance systems as they support the monitoring personnel. However, this trend is accompanied by an increasing demand on smarter camera networks carrying out surveillance tasks autonomously. Thus, there is a higher system complexity so that requirements on the video analysis algorithms are increasing as well. In this paper, we present a system concept and application for anonymously gathering, processing and analysis of trajectories in distributed smart camera networks. It allows a multitude of analysis techniques such as inspecting individual properties of the observed movement in real-time. Additionally, the anonymous movement data allows long-term storage and big data analyses for statistical purposes. The system described in this paper has been implemented as prototype system and deployed for proof of concept under real conditions at the entrance hall of the Leibniz University Hannover. It shows an overall stable performance, particularly with respect to significant illumination changes over hours, as well as regarding the reduction of false positives by post processing and trajectory merging performed on top of a panorama based person detection module.
机译：跟踪人员和对其轨迹进行分析是监视系统的重要任务，因为它们支持监视人员。但是，这种趋势伴随着对自动执行监视任务的更智能摄像机网络的需求不断增长。因此，存在较高的系统复杂度，因此对视频分析算法的要求也在增加。在本文中，我们提出了在分布式智能相机网络中匿名收集，处理和分析轨迹的系统概念和应用。它允许使用多种分析技术，例如实时检查观察到的运动的各个属性。此外，匿名移动数据允许长期存储和大数据分析，以用于统计目的。本文描述的系统已实现为原型系统，并已在汉诺威的莱布尼兹大学入口大厅实际环境中用于概念验证。它显示出总体稳定的性能，尤其是在几个小时内的照明变化方面，以及通过在基于全景的人检测模块的顶部执行后处理和轨迹合并来减少误报方面。

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