ARTICLES

Real-time Monitoring for the Regional Crowds Status

Expand
  • Key Laboratory of Virtual Geographical Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China

Received date: 2012-11-01

  Revised date: 2012-12-10

  Online published: 2012-12-25

Abstract

With the rapid development of the social economy, the massive crowd gathering appears frequently. Personnel casualties often caused by higher crowd density. So, video surveillance technology has become a national policy in many countries. Surveillance cameras have been installed in various important places of the city. Real-time monitoring of the crowds status in crowd gathering area can provide important basis for crowd management and emergency warning. Existing video-based crowd analysis can only monitor crowd status for each camera separately. We cannot get the spatial-temporal patterns of regional crowd status from a spatial perspective. In this paper, we proposed a video-GIS framework for crowd analysis. Video frames can be mapped to geographic space based on the video-GIS framework. So we can process crowd images and extract crowd density, crowd movement vector field in GIS. Then the crowd movement pattern and the main direction of crowd movement can be acquired by the vector field analysis. Finally, we design and implement a real-time monitoring system for the regional crowd status using video surveillance system and GIS. Experimental results show that: (1) previous crowd analysis methods based on the image space can only measure results by the unit of pixels. It requires further conversion if we want to get the real value. But we can get the real value directly when we process crowd images in GIS using the method we proposed. (2) The accuracy of the pixel-based low-density crowd counting estimation results can be up to 90%. The classification accuracy of the high-density crowd levels support vector machine classifier is more than 95%. So, they can fully meet the needs of crowd monitoring. (3) We can get the crowd movement pattern and the main movement direction by the analysis of crowd movement vector field in GIS. Also, we can obtain the speed of the crowd in different directions. These crowd characters all can be expressed in GIS. (4) The system we developed for the crowd monitoring can be applied to crowd management and emergency warning. It can provide decision making basis for emergencies prevention and crowd divert.

Cite this article

SONG Hong-Quan, LIU Hua-Jun, LV Guo-Nian, ZHANG Xin-Guo . Real-time Monitoring for the Regional Crowds Status[J]. Journal of Geo-information Science, 2012 , 14(6) : 686 -692,697 . DOI: 10.3724/SP.J.1047.2012.00686

References

[1] 肖坦,杨栩,李黄煌.基于视频监控的铁路客运站人群密度分析算法[J].铁道通信信号,2010,46(8):80-82.

[2] 杨琳,苗振江.一种人群异常行为检测系统的设计与实现[J].铁路计算机应用, 2010,19(7):37-41.

[3] 贾永华,呼志刚,浦世亮.基于视频前景边缘和特征检测的人群密度估计方法[J].中国公共安全(综合版), 2011,(5):180-184.

[4] 吴晟,葛万成.基于可变矩形框的人群密度估计算法[J].通信技术,2011,44(10):63-65.

[5] Ma R, Li L, Huang W, et al. On pixel count based crowd density estimation for visual surveillance [C]. Proc. IEEE Conf. Cybernetics and Intelligent Systems, Singapore, 2004.

[6] 刘福美,黎宁,张燕,等.一种基于图像处理的人群密度估计方法[J].计算机与数字工程,2011,39(5):118-122.

[7] 麻文华,黄磊,刘昌平.基于置信度分析的人群密度等级分类模型[J].模式识别与人工智能,2011,24(1):30-39.

[8] Rittsche J, Tu P H, Krahnstoeve N. Simultaneous estimation of segmentation and shape [C]. Proc. Computer Vision and Pattern Recognition, Washington, DC, 2005.

[9] 王尔丹.人群运动与密度估计技术研究[J].安全,2005,(3):21-24.

[10] Jacques Junior J C S, Muse S R, Jung C R. Crowd analysis using computer vision techniques[J]. IEEE Signal Processing Magazine, 2010, 27(5): 66-77.

[11] Lippman A. Movie maps: An application of the optical videodisc to computer graphics[J]. SIGGRAPH'80, 1980, 14 (3): 32-43.

[12] Stefanakis E, Peterson M. Geographic hypermedia: Concepts and systems[M]. Lecture Notes in Geoinformation and Cartography, Springer, 2006, 1-21.

[13] Klamma R, Spaniol M, Jarke M, et al. A hypermedia Afghan sites and monuments database[M]. Lecture Notes in Geoinformation and Cartography, Springer, 2006, Part III: 189-209.

[14] Berry J K. Capture ‘where’ and ‘when’ on video-based GIS[J]. GeoWorld, 2000(9): 26-27.

[15] Kim K H, Kim S S, Lee S H. The interactive geographic video [C]. IEEE International Geoscience and Remote Sensing Symposium, Japan, 2003.

[16] Navarrete T, Blat J. VideoGIS: Segmenting and indexing video based on geographic information [C]. The 5th AGILE Conference on Geographic Information Science, Palma, Spain, 2002.

[17] Hwang T H, Choi K H, Joo I H, et al. MPEG-7 metadata for video-based GIS applications [C]. Japan, 2003.

[18] Lee S Y, Kim S B, Choi J H. Joo I H. Design and implementation of 4S-Van: A mobile mapping system [J]. ETRI Journal, 2006(3): 256-273.

[19] 唐冰,周美玉. 基于视频图像的既有线路地理信息系统[J]. 铁路计算机应用,2001,10(11):31-33.

[20] 孔云峰.地理视频数据模型设计及网络视频GIS实现[J].武汉大学学报(信息科学版),2010,35(2):133-137.

[21] 宋宏权,孔云峰.Adobe Flex框架中的视频GIS系统设计与开发[J].武汉大学学报(信息科学版).2010, 35(6):743-746.

[22] 丰江帆,张宏,沙月进.GPS 车载移动视频监控系统的设计[J].测绘通报,2007,2:51-53.

[23] 宋宏权,刘学军,闾国年,等.基于视频的地理场景增强表达研究[J].地理与地理信息科学,2012,28(5):6-9.

[24] 李德仁,郭晟,胡庆武. 基于3S集成技术的LD2000系列移动道路测量系统及其应用[J].测绘学报,2008,37(3):272-276.

[25] Marana A N, Velastin S A, Costa L F, et al. Automatic estimation of crowd density using texture[J]. Safety Science, 1998, 28(3): 165-175.

[26] Au S Y, Ryan M C, Carey M S, et al. Managing crowd safety in public venues: A study to generate guidance for venue owners and enforcing authority inspectors [R]. HSE Contract Research Report,1993(53):993.

Outlines

/