Video shot boundary detection method based on pre-processing

doi:10.11772/j.issn.1001-9081.2014.11.3327

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (11): 3327-3331.DOI: 10.11772/j.issn.1001-9081.2014.11.3327

Previous Articles Next Articles

Video shot boundary detection method based on pre-processing

ZHANG Yikui¹,ZHAO Hui²

1. School of Computer Software, Tianjin University, Tianjin 300072, China;
2. School of Computer Science and Technology, Tianjin University, Tianjin 300072, China

Received:2014-05-22 Revised:2014-07-07 Online:2014-11-01 Published:2014-12-01
Contact: ZHANG Yikui

基于预处理的视频镜头边界检测算法

章亦葵¹,赵晖²

1. 天津大学软件学院,天津 300072;
2. 天津大学计算机科学与技术学院,天津 300072

通讯作者: 章亦葵
作者简介:
章亦葵(1965-)男,湖南长沙人,副教授,博士,主要研究方向:嵌入式系统、图像处理、数据挖掘、智能交通系统;赵晖(1990-)男,山西朔州人,硕士,主要研究方向:视频检索、图像处理。

Abstract

Abstract:

To solve the high consumption problem of fast video Shot Boundary Detection (SBD), an improved Shot Boundary Detection (SBD) method was proposed based on video pre-processing. Adaptive threshold was taken to select the candidate segments which may contain shot boundaries, the beginning frame was detected by comparing the similarity value between the first frame and the rest frames in the candidate segments, and then cut was detected immediately. Gradual transition would be detected if no cut was detected. Candidate segments had to be adjusted to ensure the whole transition was located in the same segment. Ending frame was confirmed by comparing the similarity value between the beginning frame and the rest frames in segment. Experimental results demonstrate that the proposed algorithm achieves an accuracy above 90% and the time cost is reduced 15.6%-30.2% compared with inverted triangle pattern matching method. The proposed algorithm satisfies the need of accuracy and improves detection speed compared with the traditional methods which need detection for both cut and gradual transition.

摘要：

针对视频镜头边界检测的高时耗问题,提出了一种基于视频预处理的视频镜头边界检测(SBD)改进算法。通过使用自适应的阈值选择可能包含镜头边界的候选段,候选段内首帧与其余各帧进行相似度对比检测出镜头起始帧,并立即检测切变。若候选段中不包含切变,则进行渐变检测。调整候选段以保证镜头边界位于同一段内,段内其余各帧与起始帧进行相似度对比确定镜头结束帧。实验结果表明,所提算法镜头边界识别准确率能够达到90%以上,且与倒三角模式匹配方法相比能够节约时间15.6%~30.2%；与对渐变和切变分别检测的算法相比,该算法能够在满足识别率的基础上提升检测速度。

CLC Number:

TP391.4

ZHANG Yikui ZHAO Hui. Video shot boundary detection method based on pre-processing[J]. Journal of Computer Applications, 2014, 34(11): 3327-3331.

章亦葵赵晖. 基于预处理的视频镜头边界检测算法[J]. 计算机应用, 2014, 34(11): 3327-3331.

References

[1]COTSACES C, NIKOLAIDIS N, PITAS I. Video shot detection and condensed representation: a review[J]. IEEE Signal Processing Magazine, 2006, 23(2): 28-37.
[2]YUAN J H, WANG H Y, XIAO L, et al. A formal study of shot boundary detection[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2007, 17(2): 168-186.
[3]HANJALIC A. Shot boundary detection: unraveled and resolved?[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2002, 12(2): 90-105.
[4]CAI C, LAM K M, TAN Z. A unified shot boundary detection method based on linear prediction with Bayesian cost functions[C]// Proceedings of the 2005 IEEE International Workshop on VLSI Design and Video Technology. Piscataway: IEEE Press, 2005: 101-104.
[5]JANWE N J, BHOYAR K K. Video shot boundary detection based on JND color histogram [C]// Proceedings of the 2013 IEEE the Second International Conference on Image Information Processing. Piscataway: IEEE Press, 2013: 476-480.
[6]FU Q, ZHANG Y C, XU L Y, et al. A method of shot-boundary detection based HSV space[C]// Proceedings of the 2013 9th International Conference on Computational Intelligence and Security. Piscataway: IEEE Press, 2013: 219-223.
[7]HUO Y, ZHANG P Z, WANG Y F. Adaptive threshold based video shot boundary detection framework[C]// Proceedings of the 2012 International Conference on Image Analysis and Signal Processing. Piscataway: IEEE Press, 2012: 1-5.
[8]SUN X M, LV X Y, ZHANG M W. Novel shot boundary detection method based on support vector machine[C]// Proceedings of the 2010 International Conference on Computer and Information Application. Piscataway: IEEE Press, 2010: 56-59.
[9]HAO X, TONG R F. An improved moving-window method for shot boundary detection[C]// Proceedings of the 2009 International Conference on Information Engineering and Computer Science. Piscataway: IEEE Press, 2009: 1-5.
[10]QIN T F, GU J Y, CHEN H T. A fast shot boundary detection based on k-step slipped window[C]// Proceedings of the 2010 2nd IEEE International Conference on Network Infrastructure and Digital Content. Piscataway: IEEE Press, 2010: 190-195.
[11]BESCOS J, CISNEROS G, MARTINEZ J M, et al. A unified model for techniques on video-shot transition detection[J]. IEEE Transactions on Multimedia, 2005, 7(2): 293-307.
[12]GRANA C, CUCCHIARA R. Linear transition detection as a unified shot detection approach[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2007, 17(4): 483-489.
[13]LI Y-N, LU Z-M, NIU X-M. Fast video shot boundary detection framework employing pre-processing techniques[J]. IET Image Processing, 2009, 3(3): 121-134.
[14]LU Z-M, YONG S. Fast video shot boundary detection based on SVD and pattern matching[J]. IEEE Transactions on Image Processing, 2013, 22(12): 5136-5146.

[1]	. Multiscale residual UNet based on attention mechanism to realize breast cancer lesion segmentation [J]. Journal of Computer Applications, 0, (): 0-0.
[2]	. EE-GAN：Facial expression recognition method based on generative adversarial network and network integration [J]. Journal of Computer Applications, 0, (): 0-0.
[3]	. Point cloud registration algorithm based on residual attention mechanism [J]. Journal of Computer Applications, 0, (): 0-0.
[4]	. Low contrast pulp filament defect detection method of non-woven fabric based on deep feature fusion [J]. Journal of Computer Applications, 0, (): 0-0.
[5]	PEI Yiyao, GUO Huiming, ZHANG Danpu, CHEN Wenbo. Robust 3D object detection method based on localization uncertainty [J]. Journal of Computer Applications, 2021, 41(10): 2979-2984.
[6]	XU Xuebin, ZHANG Jiada, LIU Wei, LU Longbin, ZHAO Yuqing. High-precision classification method for breast cancer fusing spatial features and channel features [J]. Journal of Computer Applications, 2021, 41(10): 3025-3032.
[7]	WANG Hongyu, ZHANG Yu, YANG Heng, MU Nan. Salient object detection in weak light images based on ant colony optimization algorithm [J]. Journal of Computer Applications, 2021, 41(10): 2970-2978.
[8]	WEI Chunwu, ZHAO Juanjuan, TANG Xiaoxian, QIANG Yan. Knowledge extraction method for follow-up data based on multi-term distillation network [J]. Journal of Computer Applications, 2021, 41(10): 2871-2878.
[9]	HAN Jiandong, LI Xiaoyu. Pedestrian re-identification method based on multi-scale feature fusion [J]. Journal of Computer Applications, 2021, 41(10): 2991-2996.
[10]	ZHANG Kaiyue, ZHANG Hong. Shipping monitoring image recognition model based on attention mechanism network [J]. Journal of Computer Applications, 2021, 41(10): 3010-3016.
[11]	LI Ziqiang, WANG Zhengyong, CHEN Honggang, LI Linyi, HE Xiaohai. Video abnormal behavior detection based on dual prediction model of appearance and motion features [J]. Journal of Computer Applications, 2021, 41(10): 2997-3003.
[12]	GAO Shiwei, ZHANG Changzhu, WANG Zhuping. Lightweight real-time semantic segmentation algorithm based on separable pyramid [J]. Journal of Computer Applications, 2021, 41(10): 2937-2944.
[13]	ZHUO Yuxin, HAN Suya, ZHANG Yufeng, LI Zhiyao, DONG Yifeng. Automatic segmentation method of microwave ablation region based on Nakagami parameters images of ultrasonic harmonic envelope [J]. Journal of Computer Applications, 2021, 41(10): 3089-3096.
[14]	. Noctiluca scintillans red tide extraction method based on UAV images and deep learning [J]. Journal of Computer Applications, 0, (): 0-0.
[15]	. Multi-label image classification method based on global and local label relationship [J]. Journal of Computer Applications, 0, (): 0-0.

Video shot boundary detection method based on pre-processing

基于预处理的视频镜头边界检测算法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics