|
|
- 2015
基于大数据的热点区域人员流量实时监测系统
|
Abstract:
摘要 采用大数据技术实现一个热点区域人员流量的实时监测系统.该系统分析热点区域内每个出入口实时人员流量,挖掘各热点区域驻留人员数量规律,从而科学地安排资源及运营管理.整个处理过程耗时少于15 s,误差小于7.5%.运营结果表明该系统达到了大数据的实时采集、实时分析处理和实时分区控制的可实用目标.
| [1] | Suna S W, Wang Y C, Huang F, et al. Moving foreground object detection via robust SIFT trajectories[J]. Journal of Visual Communication and Image Representation, 2013, 24(3):232-243. |
| [2] | Toshev A, Taskar B, Daniilidis K. Shape-based object detection via boundary structure segmentation[J]. International Journal of Computer Vision archive, 2012, 99 (2):123-146. |
| [3] | Lucas B D, Kanade T. An iterative image registration technique with an application to stereo vision[C]//Proceedings of Imaging Understanding Workshop, 1981:121-130. |
| [4] | Pérez J S, Meinhardt-Llopis E, Facciolo G. TV-L1 optical flow estimation[J]. Image Processing on Line, 2013, 3:137-150. |
| [5] | <p> 覃雄派,王会举,杜小勇,等.大数据分析:RDBMS与MapReduce 的竞争与共生[J]. 软件学报,2012,23(1):32-45. |
| [6] | Wlodarczyk T W. Overview of time series storage and processing in a cloud environment[C]//IEEE 4th International Conference on Digital Object Identifier: 10.1109/CloudCom. 2012:625-628. |
| [7] | Storm. distributed and fault tolerant realtime computation[CP/OL].[2014-03-10]. http://storm-project.net.</p> |
