面向智能监控摄像头的监控视频大数据分析处理

邵振峰; 蔡家骏; 王中元; 马照亭

doi:10.11999/JEIT160712

面向智能监控摄像头的监控视频大数据分析处理

doi: 10.11999/JEIT160712

基金项目:

中央高校基本科研业务费专项资金(2042016kf0179, 2042016kf1019, 2042016gf0033)，2016年广州市科技计划资助项目(201604020070)，测绘地理信息公益性行业科研专项经费项目(201512027)，湖北省自然科学基金(2015CFB406)，武汉市应用基础研究计划项目(2016010101010025)

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出版历程
- 收稿日期: 2016-07-07
- 修回日期: 2016-12-13
- 刊出日期: 2017-05-19

Analytical Processing Method of Big Surveillance Video Data Based on Smart Monitoring Cameras

Funds:

The Fundamental Research Funds for the Central Universities (2042016kf0179, 2042016kf1019, 2042016 gf0033), The Guangzhou Science and Technology Project (2016- 04020070), The Special Funds Project on Public Welfare Industry Research of Surveying and Mapping Geographic Information (201512027), The Natural Science Fund of Hubei Province (2015CFB406), The Applied Basic Research Program of Wuhan City (2016010101010025)

摘要

摘要: 视频监控是安防的重要组成部分，智能监控摄像头以其丰富的异常行为识别功能，极大地增强了监控场所的安全。随着部署的智能摄像头日渐增多以及视频监控网规模的不断扩大，海量的视频数据给存储、检索及分析带来了巨大挑战。该文提出智能摄像头异常报警事件驱动的监控视频大数据智能处理方法，具体包括：多点关联分析的异常事件自动预警、事件驱动的监控视频选择性存储以及异常行为事件约束的关联检索，以期提高大数据时代监控视频数据的深度利用效率。实践案例证实，所提方法能够实现异常事件的可信预警，录像视频选择性的高效保存和破案线索的快速发现。
- 监控视频大数据 /
- 关联分析 /
- 异常事件预警 /
- 智能检索 /
- 智能监控系统
Abstract: As an important part in the security and protection system of cities, smart monitoring cameras which are equipped with intelligent video analytics ability can monitor in different scenes and pre-alarm abnormal behaviors or events. Nevertheless, with the growing number of smart monitoring cameras, the challenges to analytics, storage and retrieval of massive surveillance video data need to be solved in the big data era. This paper proposes an intelligent processing method which makes full use of smart cameras to big surveillance video data. The method consists of three parts: the intelligent pre-alarming for abnormal events, smart storage for surveillance video and rapid retrieval for evidence videos, which aim to improve the utilization efficiency of surveillance video data. Experimental results prove that the proposed approach can reliably pre-alarm abnormal events, efficiently reduce storage space of recorded video and significantly improve the evidence video retrieval rates associated with specific suspects.
- Big surveillance video data /
- Association analysis /
- Pre-alarming of abnormal events /
- Intelligent retrieval /
- Intelligent surveillance system

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参考文献(17)

DBOUK M, MCHEICK H, and SBEITY I. CityPro; an integrated city-protection collaborative platform[J]. Procedia Computer Science, 2014, 37: 72-79. doi: 10.1016/j.procs.2014.　08.014

黄凯奇, 陈晓棠, 康运锋, 等. 智能视频监控技术综述[J]. 计算机学报, 2015, 38(6): 1093-1118. doi: 10.11897/SP.J.1016. 2015.01093.

HUANG Kaiqi, CHEN Xiaotang, KANG Yunfeng, et al. Intelligent visual surveillance: A review[J]. Chinese Journal of Computers, 2015, 38(6): 1093-1118. doi: 10.11897/SP.J.1016. 2015.01093.

张诚, 马华东, 傅慧源. 基于时空关联图模型的视频监控目标跟踪[J]. 北京航空航天大学学报, 2015, 41(4): 713-720. doi: 10.13700/j.bh.1001-5965.2014.0472.

ZHANG Cheng, MA Huadong, and FU Huiyuan. Object tracking in surveillance videos using spatial-temporal correlation graph model[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(4): 713-720. doi: 10.13700/j.bh.1001-5965.2014.0472 .

GUO Xiaoyan, CAO Yu, and TAO Jun. SVIS: Large Scale Video Data Ingestion into Big Data Platform[M]. Hanoi: Springer, 2015: 300-306.

SHVACHKO K, KUANG H, RADIA S, et al. The hadoop distributed file system[C]. IEEE 26th Symposium on MASS Storage Systems and Technologies, Nevada, 2010: 1-10. doi: 10.1109/MSST.2010.5496972.

KIM H, LEE S, KIM Y, et al. Weighted joint-based human behavior recognition algorithm using only depth information for low-cost intelligent video-surveillance system[J]. Expert Systems with Applications, 2016, 45(C): 131-141. doi: 10.1016 /j.eswa.2015.09.035.

CHEN Chunyu, SHAO Yu, and BI Xiaojun. Detection of anomalous crowd behavior based on the acceleration feature[J]. IEEE Sensors Journal, 2015, 15(12): 7252-7261. doi: 10.1109/JSEN.2015.2472960.

CALDERARA S, CUCCHIARA R, and PRATI A. Detection of abnormal behaviors using a mixture of Von Mises distributions[C]. IEEE International Conference on Advanced Video and Signal Based Surveillance, London, 2007: 141-146. doi: 10.1109/AVSS.2007.4425300.

BALLAN L, BERTINI M, DEL BIMBO A, et al. Effective codebooks for human action categorization[C]. IEEE 12th International Conference on Computer Vision Workshops, Kyoto, 2009: 506-513. doi: 10.1109/ICCVW.2009.5457658.

WANG Kejun and OLUWATOYIN P P. Ant-based clustering of visual-words for unsupervised human action recognition[C]. World Congress on Nature and Biologically Inspired Computing, Fukuoka, 2010: 654-659. doi: 10.1109/NABIC. 2010.5716377.

COLLINS R T, LIPTON A J, KANADE T, et al. A system for video surveillance and monitoring[R]. Technical report CMU-RI-TR-00-12, Pittsburgh: Camegie Mellon University, 2000.

HUANG Kaiqi and TAN Tieniu. Vs-star: A visual interpretation system for visual surveillance[J]. Pattern Recognition Letters, 2010, 31(14): 2265-2285. doi: 10.1016/j. patrec. 2010.05.029.

GARCIA C R, QUESADAARENCIBIA A, CRISTOBAL T, et al. An intelligent system proposal for improving the safety and accessibility of public transit by highway[J]. Sensors, 2015, 15(8): 20279-20304. doi: 10.3390/s150820279.

HANCKE G P, SILVA B C, and HANCKE G P. The role of advanced sensing in smart cities[J]. Sensors, 2012, 13(1): 393-425. doi: 10.3390/s130100393.

HELBING D and BALIETTI S. From social data mining to forecasting socio-economic crises[J]. European Physical Journal Special Topics, 2011, 195(1): 3-68. doi: 10.1140/ epjst/e2011-01401-8.

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