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山东大学学报 (工学版) ›› 2020, Vol. 50 ›› Issue (1): 8-13.doi: 10.6040/j.issn.1672-3961.0.2019.276

• 机器学习与数据挖掘 • 上一篇    下一篇

基于ViBe算法运动特征的关键帧提取算法

李秋玲(),邵宝民*(),赵磊,王振,姜雪   

  1. 山东理工大学计算机科学与技术学院, 山东 淄博 255049
  • 收稿日期:2019-06-01 出版日期:2020-02-20 发布日期:2020-02-14
  • 通讯作者: 邵宝民 E-mail:liqiuling176@163.com;bmshao@sdut.edu.cn
  • 作者简介:李秋玲(1992-),女,山东泰安人,硕士研究生,主要研究方向为计算机视觉. E-mail: liqiuling176@163.com
  • 基金资助:
    国家自然科学基金资助项目(61841602);山东省自然科学基金资助项目(ZR2018PF005)

Key frame extraction based on ViBe algorithm for motion feature extraction

Qiuling LI(),Baomin SHAO*(),Lei ZHAO,Zhen WANG,Xue JIANG   

  1. College of Computer Science and Technology, Shandong University of Technology, Zibo 255049, Shandong, China
  • Received:2019-06-01 Online:2020-02-20 Published:2020-02-14
  • Contact: Baomin SHAO E-mail:liqiuling176@163.com;bmshao@sdut.edu.cn
  • Supported by:
    国家自然科学基金资助项目(61841602);山东省自然科学基金资助项目(ZR2018PF005)

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摘要：

针对视频关键帧提取算法中运动类视频运动目标特征不易提取所造成的错选和漏选问题,提出一种基于背景建模(visual background extractor, ViBe)算法的前景运动目标特征提取的关键帧提取算法。通过ViBe算法对视频序列进行前景目标检测,提取前景运动目标的尺度不变特征变换(scale invariant feature transform, SIFT)特征,并对相邻帧之间的特征数据进行特征点匹配,根据定义的公式计算视频帧的相似度,然后根据提出的关键帧判别方法输出视频的关键帧。试验结果表明,该算法能较好的解决运动类视频关键帧提取中出现的漏选和错选问题,与基于SIFT分布直方图的算法相比,其查准率和查全率的综合指标F1值有较好提高。因此该算法对于判别运动类视频中包含关键动作的关键帧具有较好的检测效果。

关键词: 关键帧提取, 视频序列背景检测算法, 尺度不变特征变换, 目标特征, 特征点匹配

Abstract:

Aiming at the fact that the background was dominant in the key frame extraction algorithm, in which the foreground target was too small and it was not easy to extract the features of moving targets in sports video, a key frame extraction algorithm for foreground moving target feature extraction based on background modeling algorithm was proposed, which was called visual background extractor (ViBe) algoritm. The foreground target detection of video sequence was firstly carried out using ViBe algorithm, afterwards the scale-invariant feature transformation (SIFT) features of the foreground moving target were extracted. Based on the similarity calculated from video frame series, the key frames of video were output according to the key frame discrimination method. The experimental results showed that the proposed algorithm could solve the problem of missed selection and misselection in traditional key frame extraction. Compared with the algorithm based on SIFT distribution histogram, the F1 score was well improved. The algorithm based on ViBe could effectively identify key frames in sports video.

Key words: key frame extraction, background modeling, scale invariant feature transform, target characteristic, feature point matching

中图分类号: 

  • TP37

图1

ViBe分类图"

图2

ViBe更新图示"

图3

运动物体的SIFT特征"

图4

关键帧提取算法流程图"

表1

试验视频信息"

视频名称 总帧数/帧 镜头数/个 时长/s
体操视频 7 594 55 303.76
足球视频 5 124 17 204.96
羽毛球视频 3 750 19 50.00
篮球视频 4 432 19 177.28
击剑视频 2 762 24 110.48

图5

体操视频提取结果"

图6

足球视频提取结果"

图7

羽毛球视频提取结果"

图8

篮球视频提取结果"

表2

更改参数前后对比结果"

方法 体操 足球 羽毛球 篮球 击剑
漏选 错选 冗余 漏选 错选 冗余 漏选 错选 冗余 漏选 错选 冗余 漏选 错选 冗余
A 43 37 18 10 8 6 13 9 10 18 15 19 20 14 6
B 45 38 22 11 8 8 14 9 9 19 17 20 20 15 7

表3

本文算法与其他文献算法对比结果"

方法 体操 足球 羽毛球篮球击剑
漏选 错选 选对 漏选 错选 选对 漏选 错选 选对 漏选 错选 选对 漏选 错选 选对
C 43 37 184 10 8 50 13 9 42 18 15 68 20 14 58
D 50 41 177 13 9 47 14 10 41 20 18 66 23 20 55

图9

本研究算法与SIFT分布直方图算法对比结果"

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