[1]黄 涛,苏松源*,杜长青,等.一种基于视频的多目标追踪与分割算法[J].计算机技术与发展,2021,31(03):95-99.[doi:10. 3969 / j. issn. 1673-629X. 2021. 03. 016]
　HUANG Tao,SU Song-yuan*,DU Chang-qing,et al.A Multi-target Tracking and Segmentation Algorithm Based on Video[J].,2021,31(03):95-99.[doi:10. 3969 / j. issn. 1673-629X. 2021. 03. 016]
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一种基于视频的多目标追踪与分割算法()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年03期
页码:
95-99
栏目:
图形与图像
出版日期:
2021-03-10

文章信息/Info

Title:
A Multi-target Tracking and Segmentation Algorithm Based on Video
文章编号:
1673-629X(2021)03-0095-05
作者:
黄 涛1; 2; 苏松源2; 3*; 杜长青1; 诸雅琴2; 陈 勇1
1. 国网江苏省电力工程咨询有限公司,江苏 南京 210024;
2. 教育部网络与信息集成重点实验室(东南大学),江苏 南京 210096
3. 东南大学 网络空间安全学院,江苏 南京 210000
Author(s):
HUANG Tao1; 2; SU Song-yuan2; 3*; DU Chang-qing1; ZHU Ya-qin2; CHEN Yong1
1. State Grid Jiangsu Electric Power Engineering Consulting Co. ,Ltd. ,Nanjing 210024,China;
2. School of Cyber Science and Engineering,Southeast University,Nanjing 210096,China;
3. School of Cyberspace Security,Southeast University,Nanjing 210000,China
关键词:
计算机视觉; 目标追踪; 光流分析法; 视频关键帧; 分割精度
Keywords:
computer vision; target tracking; optical flow analysis; video keyframe; segmentation accuracy
分类号:
TP399
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 03. 016
摘要:
基于视觉的多目标跟踪由于在智能监控、动作与行为分析、自动驾驶、虚拟现实和娱乐互动等领域都有重要的应用,近年来越来越多地成为计算机视觉领域的研究重点。 并且在电力设施中对人员的活动需要实时追踪,有助于安全防护。 对此,针对视频中的多目标检测与分割问题,在原有 Mask-RCNN 算法的基础上做了改进,引入光流分析法和视频关键帧提取技术,在不改变检测精度的同时大幅度缩短对每一帧的检测时间。 实验结果表明,相较于原有 Mask-RCNN 算法,改进的 Mask-RCNN 算法大幅缩短了检测时间, 对比于其他的目标追踪算法,改进的 Mask-RCNN 算法增强了对视频中的对象实例识别和分割的效果,分割精度有了显著提升,达到了视频里的多目标追踪的需求,并且对提高多目标场景下的目标跟踪水平具有一定的实际意义。
Abstract:
Multi-target tracking based on vision has become an important research focus in the field of computer vision? in recent years due to its important applications in the fields of intelligent monitoring,motion and behavior analysis, autonomous driving,virtual reality and entertainment interaction. And in the power facility, the activities of personnel need to be tracked in real time,which is helpful for safety protection. In this regard,the multi-target detection and segmentation problem in video is improved on the basis of the original MaskRCNN algorithm. The optical flow analysis method and video key frame extraction technology are introduced,which greatly shortens the detection time of each frame without changing the detection accuracy. Experiment shows that compared with the original Mask-RCNN algorithm,the improved Mask -RCNN algorithm has been greatly shortened in detection time. Compared with other target tracking algorithms,the improved Mask -RCNN algo-rithm enhances the effect of the object instance recognition and segmentation in video,significantly improving the segmentation accuracy,which meets the needs of multi-target tracking in video,and has certain practical significance for improving the target tracking level in multi-target scenarios.

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更新日期/Last Update: 2020-03-10