| 基于动态帧间间隔更新的人群异常行为检测 |
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| 引用本文： | 陈颖熙,廖晓东,钟帅.基于动态帧间间隔更新的人群异常行为检测[J].计算机系统应用,2018,27(2):207-211. |
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| 作者姓名： | 陈颖熙 廖晓东 钟帅 |
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| 作者单位： | 福建师范大学 光电与信息工程学院, 福州 350007,福建师范大学 光电与信息工程学院, 福州 350007;福建师范大学 医学光电科学与技术教育部重点实验室, 福州 350007;福建师范大学 福建省先进光电传感与智能信息应用工程技术研究中心, 福州 350007,福建师范大学 光电与信息工程学院, 福州 350007 |
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| 基金项目： | 省科技厅区域科技重大项目（2015H4007） |
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| 摘 要： | 针对视频监控中人群异常行为检测方面存在的实时性和准确性问题，本文基于金字塔LK光流法提出一种动态帧间间隔更新的人群异常行为检测的方法. 该算法通过提取的人群运动信息来动态更新帧间间隔，接着以该帧间间隔来检测人群运动信息. 这样，算法不仅保留了原算法在检测人群运动信息方面优点，且有效提高了算法的运行效率. 最后，该算法通过获取的人群运动矢量交点密集度及能量信息来识别人群异常行为. 对多个视频进行测试，测试结果表明，该算法能够以较高正确率识别视频中人群的异常行为，同时还有效提高了算法的运行速度.
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| 关 键 词： | 人群恐慌 行人检测 交点密集度 动态帧间间隔 |
| 收稿时间： | 2017/5/22 0:00:00 |
| 修稿时间： | 2017/6/8 0:00:00 |
Abnormal Crowd Behavior Detection Based on Dynamic Interframe Spacing Updating |
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| CHEN Ying-Xi,LIAO Xiao-Dong and ZHONG Shuai.Abnormal Crowd Behavior Detection Based on Dynamic Interframe Spacing Updating[J].Computer Systems& Applications,2018,27(2):207-211. |
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| Authors: | CHEN Ying-Xi LIAO Xiao-Dong and ZHONG Shuai |
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| Affiliation: | College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China,College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China;Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou 350007, China;Fujian Provincial Engineering Research Center for Optoelectronic Sensors and Intelligent Information, Fuzhou 350007, China and College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China |
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| Abstract: | In order to detect the abnormal crowd behavior in video surveillance in real time and more accurate, this study proposes a method of dynamic interframe space updating based on the Pyramid LK optical flow. The algorithm dynamically updates the interframe interval by extracting the crowd motion information, and then detects the crowd motion information at the interframe interval. In this way, the algorithm does not only preserve the advantages of the traditional algorithm in detecting crowd motion information, but also improves the efficiency. Finally, the algorithm identifies the abnormal crowd behavior by acquiring the intersection density and energy information of the crowd motion vector. By testing multiple videos, the test results show that the algorithm can identify the abnormal crowd behavior in the video with high accuracy, and also effectively improves the running speed. |
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| Keywords: | panic crowd pedestrian detection intersection density dynamic interframe space |
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