区域人群状态与行为的时空感知方法
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摘要
公众聚集场所人群高度聚集、流动性大,构成了具有动态性、不确定性等特点的复杂地理场景。高密度聚集、流动的人群隐藏着巨大的安全隐患,时常发生拥挤踩踏等突发公共事件。突发公共事件应急管理是保障公共安全的核心问题,所以,如何快速感知与监控突发公共事件是国内外公共安全保障重点关注的热点问题。随着社会的发展,世界各国政府已将视频监控技术的研究与应用上升到战略高度,从政策、法律、经济等方面给予大力支持。
然而,目前基于视频监控系统的群体突发事件感知处理智能化水平有待进一步提升,特别是地理环境下的视频分析、行为感知等研究,亟待发展一种集地理环境与视频分析于一体、高效准确的新型感知模式和诊断技术,以满足对突发公共事件的管理和应对需求。鉴于此,本文以视频数据与GIS数据的协同分析和聚合理解为科学手段,拟突破地理环境下区域人群状态与行为的感知等关键科学问题,发展视频分析与空间分析耦合的区域人群状态与行为感知方法,取得了以下研究成果：
(1)提出了一种可跨摄像机的自适应人群密度估计方法。现有基于视频分析的人群密度估计模型具有较强的场景依赖性,无法实现人群密度估计模型的跨摄像机应用,需对各监控摄像机进行模型训练,浪费了大量的人力与物力,阻碍了布设有大量监控探头区域的人群监控应用。本文提出了一种可跨摄像机的自适应人群密度估计方法,在地理空间构建人群密度估计模型,解决了不同监控设备之间人群图像的尺度多样化问题,克服了模型的场景依赖性,大大提高了人群密度估计模型的构建效率。
(2)提出了地理环境下的群体行为模式分析方法。以往对人群运动状态的监控基于图像空间,无法感知人群的真实运动状态。为实时感知监控人群在地理环境下的真实运动状态,提出了GIS环境下的群体行为模式分析方法,在地理参考下求算人群运动矢量场,通过分析人群运动矢量场,可得到监控区域人群的群体运动模式、群体运动趋势及各方向的群体运动速度。
(3)设计了相关群体异常行为检测方法。利用地理参考下的人群运动矢量场,结合本文的群体行为模式分析方法,设计了人群骤聚、骤散、运动趋势突变、运动速率突变、逆向行走等群体异常行为检测方法,可进一步分析得到监控区域人群状态的时空热点,为突发事件的预防提供依据。
(4)构建了基于贝叶斯网络的监控盲区人群状态推演模型。监控摄像机在人群活动区域的的布设大都稀疏、离散、无重叠,无法直接获取监控盲区的人群状态。本文提出了基于贝叶斯网络的监控盲区人群状态推演模型,可利用已有的稀疏人群状态监控信息,推演监控盲区的人群状态,进而得到整个人群活动区域的人群状态空间格局。
(5)进行了区域人群状态的时空格局演化分析。利用多个时刻的人群状态空间格局,对实验区人群状态的时空格局演变进行了分析,得到了人群状态分布的时空模式,并结合其他数据进一步分析了形成人群状态时空模式的机理,可为安保人员的布控、设施规划、人群疏导、商业策略等提供决策依据。
(6)研发了区域人群状态与行为感知系统。基于以上研究成果,设计并开发了区域人群状态与行为感知系统,并以南京市夫子庙步行街地区为试验区进行了应用。
The crowd in public gathering places has the characteristics of high concentration and high mobility. This constitutes a dynamic and uncertain geographical scene. High density crowds often lead to stampede and other public emergencies, and caused massive casualties, huge economic losses and adverse social impacts. Emergency management is the core issue of ensuring public safety. So, fast perception and monitoring of public emergencies is a hot issue of public safety and security at home and abroad.
With the development of society, the research and application of video surveillance technology has become a national strategy for governments around the world. And give strong support from the policy, legal, economic and other aspects. However, the current intelligent processing level of mass emergency perception and monitoring based on video monitoring system needs to be further improved. Such as intelligent video analysis, multi-sensor cooperative work, abnormal event detection, especially video analysis and behavior perception in geographical environment. It is imperative to develop an efficient and accurate perception technology with the integration of video analysis and geographical environment to meet the management of public emergency. Consequently, in order to break through the key scientific issues of perception and monitoring for the regional crowds status and behavior in geographical environment, we combined video data with GIS data and proposed the crowd status and behavior monitoring methods. The contributions are as follows.
(1) A scene invariant method for crowd density estimation is proposed. Current crowd density estimation methods based on video analysis are scene-dependent. An estimation model acquired with video data taken by one particular camera cannot be adaptively applied to data taken by other cameras. This paper presented a scene invariant crowd density estimation method using GIS to monitor crowd size for large areas. The proposed method mapped crowd images to GIS. Then we can estimate crowd density for each camera in GIS using an estimation model obtained by one camera. It does not require additional training when deployed for crowd density estimation on a new camera. This greatly improves the efficiency of crowd density estimation modeling.
(2) We can analyze the crowd behavior in geographical environment using the method we proposed. The traditional crowd motion analysis methods are based on image space. So we cannot get the true movement state in the real world. For real-time monitoring crowd behavior in geographical environment, this paper presents crowd behavior pattern analysis methods using GIS. The measured crowd motion vector field can be calculated by crowd images in GIS. Then we can get the crowd motion pattern, crowd motion trend and crowd motion velocity by analyzing the crowd motion vector field.
(3) We proposed a crowd for abnormal behavior detection based on the geo-referenced crowd motion vector field. This method can be used to detect crowd abnormal behaviors, such as centers gathering, centers divergence, motion trend mutations, motion velocity mutations and reverse walking et al. And then we can further analyze the spatial-temporal hotspots in the monitoring area, and provide the basis for the prevention of emergencies.
(4) Surveillance cameras installed in the monitoring areas are sparse, discrete and non-overlapping. So the crowd status information in the areas with no camera cannot be captured. In this paper, we create the crowd status inference model for the areas with no camera using Bayesian network. Then the spatial pattern of the regional crowd status can be predicted by the sparse crowd status data using the inference model.
(5) We analyzed the spatial-temporal evolution process for the multi-time crowd status spatial pattern in experimental area. Then we can get the crowds spatial-temporal distribution modes, and we can further analyze the formation mechanisms of the crowd status spatial-temporal pattern. This can provide basis for security personnel deployment, facilities planning, crowd management, business strategy and so on.
(6) We designed and developed a system for the regional crowd status and behavior monitoring based on the above research results. And this system was applied to crowd monitoring in Nanjing Confucius Temple Pedestrian Street.
然而,目前基于视频监控系统的群体突发事件感知处理智能化水平有待进一步提升,特别是地理环境下的视频分析、行为感知等研究,亟待发展一种集地理环境与视频分析于一体、高效准确的新型感知模式和诊断技术,以满足对突发公共事件的管理和应对需求。鉴于此,本文以视频数据与GIS数据的协同分析和聚合理解为科学手段,拟突破地理环境下区域人群状态与行为的感知等关键科学问题,发展视频分析与空间分析耦合的区域人群状态与行为感知方法,取得了以下研究成果：
(1)提出了一种可跨摄像机的自适应人群密度估计方法。现有基于视频分析的人群密度估计模型具有较强的场景依赖性,无法实现人群密度估计模型的跨摄像机应用,需对各监控摄像机进行模型训练,浪费了大量的人力与物力,阻碍了布设有大量监控探头区域的人群监控应用。本文提出了一种可跨摄像机的自适应人群密度估计方法,在地理空间构建人群密度估计模型,解决了不同监控设备之间人群图像的尺度多样化问题,克服了模型的场景依赖性,大大提高了人群密度估计模型的构建效率。
(2)提出了地理环境下的群体行为模式分析方法。以往对人群运动状态的监控基于图像空间,无法感知人群的真实运动状态。为实时感知监控人群在地理环境下的真实运动状态,提出了GIS环境下的群体行为模式分析方法,在地理参考下求算人群运动矢量场,通过分析人群运动矢量场,可得到监控区域人群的群体运动模式、群体运动趋势及各方向的群体运动速度。
(3)设计了相关群体异常行为检测方法。利用地理参考下的人群运动矢量场,结合本文的群体行为模式分析方法,设计了人群骤聚、骤散、运动趋势突变、运动速率突变、逆向行走等群体异常行为检测方法,可进一步分析得到监控区域人群状态的时空热点,为突发事件的预防提供依据。
(4)构建了基于贝叶斯网络的监控盲区人群状态推演模型。监控摄像机在人群活动区域的的布设大都稀疏、离散、无重叠,无法直接获取监控盲区的人群状态。本文提出了基于贝叶斯网络的监控盲区人群状态推演模型,可利用已有的稀疏人群状态监控信息,推演监控盲区的人群状态,进而得到整个人群活动区域的人群状态空间格局。
(5)进行了区域人群状态的时空格局演化分析。利用多个时刻的人群状态空间格局,对实验区人群状态的时空格局演变进行了分析,得到了人群状态分布的时空模式,并结合其他数据进一步分析了形成人群状态时空模式的机理,可为安保人员的布控、设施规划、人群疏导、商业策略等提供决策依据。
(6)研发了区域人群状态与行为感知系统。基于以上研究成果,设计并开发了区域人群状态与行为感知系统,并以南京市夫子庙步行街地区为试验区进行了应用。
The crowd in public gathering places has the characteristics of high concentration and high mobility. This constitutes a dynamic and uncertain geographical scene. High density crowds often lead to stampede and other public emergencies, and caused massive casualties, huge economic losses and adverse social impacts. Emergency management is the core issue of ensuring public safety. So, fast perception and monitoring of public emergencies is a hot issue of public safety and security at home and abroad.
With the development of society, the research and application of video surveillance technology has become a national strategy for governments around the world. And give strong support from the policy, legal, economic and other aspects. However, the current intelligent processing level of mass emergency perception and monitoring based on video monitoring system needs to be further improved. Such as intelligent video analysis, multi-sensor cooperative work, abnormal event detection, especially video analysis and behavior perception in geographical environment. It is imperative to develop an efficient and accurate perception technology with the integration of video analysis and geographical environment to meet the management of public emergency. Consequently, in order to break through the key scientific issues of perception and monitoring for the regional crowds status and behavior in geographical environment, we combined video data with GIS data and proposed the crowd status and behavior monitoring methods. The contributions are as follows.
(1) A scene invariant method for crowd density estimation is proposed. Current crowd density estimation methods based on video analysis are scene-dependent. An estimation model acquired with video data taken by one particular camera cannot be adaptively applied to data taken by other cameras. This paper presented a scene invariant crowd density estimation method using GIS to monitor crowd size for large areas. The proposed method mapped crowd images to GIS. Then we can estimate crowd density for each camera in GIS using an estimation model obtained by one camera. It does not require additional training when deployed for crowd density estimation on a new camera. This greatly improves the efficiency of crowd density estimation modeling.
(2) We can analyze the crowd behavior in geographical environment using the method we proposed. The traditional crowd motion analysis methods are based on image space. So we cannot get the true movement state in the real world. For real-time monitoring crowd behavior in geographical environment, this paper presents crowd behavior pattern analysis methods using GIS. The measured crowd motion vector field can be calculated by crowd images in GIS. Then we can get the crowd motion pattern, crowd motion trend and crowd motion velocity by analyzing the crowd motion vector field.
(3) We proposed a crowd for abnormal behavior detection based on the geo-referenced crowd motion vector field. This method can be used to detect crowd abnormal behaviors, such as centers gathering, centers divergence, motion trend mutations, motion velocity mutations and reverse walking et al. And then we can further analyze the spatial-temporal hotspots in the monitoring area, and provide the basis for the prevention of emergencies.
(4) Surveillance cameras installed in the monitoring areas are sparse, discrete and non-overlapping. So the crowd status information in the areas with no camera cannot be captured. In this paper, we create the crowd status inference model for the areas with no camera using Bayesian network. Then the spatial pattern of the regional crowd status can be predicted by the sparse crowd status data using the inference model.
(5) We analyzed the spatial-temporal evolution process for the multi-time crowd status spatial pattern in experimental area. Then we can get the crowds spatial-temporal distribution modes, and we can further analyze the formation mechanisms of the crowd status spatial-temporal pattern. This can provide basis for security personnel deployment, facilities planning, crowd management, business strategy and so on.
(6) We designed and developed a system for the regional crowd status and behavior monitoring based on the above research results. And this system was applied to crowd monitoring in Nanjing Confucius Temple Pedestrian Street.
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