Browse > Article
http://dx.doi.org/10.22937/IJCSNS.2022.22.6.1

A Comparison of Scene Change Localization Methods over the Open Video Scene Detection Dataset  

Panchenko, Taras (Taras Shevchenko National University of Kyiv)
Bieda, Igor (Taras Shevchenko National University of Kyiv)
Publication Information
International Journal of Computer Science & Network Security / v.22, no.6, 2022 , pp. 1-6 More about this Journal
Abstract
Scene change detection is an important topic because of the wide and growing range of its applications. Streaming services from many providers are increasing their capacity which causes the industry growth. The method for the scene change detection is described here and compared with the State-of-the-Art methods over the Open Video Scene Detection (OVSD) - an open dataset of Creative Commons licensed videos freely available for download and use to evaluate video scene detection algorithms. The proposed method is based on scene analysis using threshold values and smooth scene changes. A comparison of the presented method was conducted in this research. The obtained results demonstrated the high efficiency of the scene cut localization method proposed by authors, because its efficiency measured in terms of precision, recall, accuracy, and F-metrics score exceeds the best previously known results.
Keywords
scene change detection; OVSD dataset; scene cut; scene break; scene localization;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Ameni Sassi, Wael Ouarda, Chokri Ben Amar, Serge Miguet. Sky-CNN: A CNN-based Learning Approach for Skyline Scene Understanding[J]. International Journal of Intelligent Systems and Applications(IJISA), 2019, 11 (4): 14-25.   DOI
2 Anupam Dey, Fahad Mohammad, Saleque Ahmed, Raiyan Sharif, A.F.M. Saifuddin Saif. Anomaly Detection in Crowded Scene by Pedestrians Behaviour Extraction using Long Short Term Method: A Comprehensive Study[J]. International Journal of Education and Management Engineering(IJEME), 2019, 9 (1): 51-63.   DOI
3 Igor Bieda, Anton Kisil, Taras Panchenko. An Approach to Scene Change Detection[C]. The 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS'2021), 2021: 489-493.
4 D. Rotman, D. Porat, G. Ashour. Robust and Efficient Video Scene Detection Using Optimal Sequential Grouping[C]. IEEE International Symposium on Multimedia (ISM), 2016: 275-280.
5 QL. Baraldi, C. Grana, R. Cucchiara. Shot and scene detection via hierarchical clustering for re-using broadcast video[C]. International Conference on Computer Analysis of Images and Patterns, 2015: 801-811.
6 L. Baraldi, C. Grana, R. Cucchiara. Analysis and re-use of videos in educational digital libraries with automatic scene detection[C]. 11th Italian Research Conference on Digital Libraries, 2015: 155-164.
7 M. Del Fabro, L. Boszormenyi. State-of-the-Art and Future Challenges in Video Scene Detection: a Survey[J]. Multimedia systems, 2013, 19(5): 427-454.   DOI
8 Lolith Gopan, E.Venkateswarlu, Thara Nair, G.P.Swamy, B.Gopala Krishna. Scene based Non-uniformity Correction for Optical Remote Sensing Imagery[J]. International Journal of Image, Graphics and Signal Processing(IJIGSP), 2017, 9 (12): 50-57.   DOI
9 P. Sidiropoulos, V. Mezaris, I. Kompatsiaris, H. Meinedo, M. Bugalho, I. Trancoso. Temporal video segmentation to scenes using high-level audiovisual features[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2011, 21 (8): 1163-1177.   DOI
10 J. Vendrig, M. Worring. Systematic evaluation of logical story unit segmentation[J]. IEEE Transactions on Multimedia, 2002, 4 (4): 492-499.   DOI
11 Igor Bieda. Scene Change Localization in Video[J]. Taras Shevchenko National University of Kyiv Visnyk, Physical and Mathematical Sciences Series, 2021, 1: 57-62.
12 D. Rotman, D. Porat, G. Ashour. Robust Video Scene Detection Using Multimodal Fusion of Optimally Grouped Features[C]. IEEE 19th International Workshop on Multimedia Signal Processing (MMSP), 2017: 44-47.
13 S. Schmiedeke, P. Xu, I. Ferrane, M. Eskevich, C. Kofler, M. A. Larson, Y. Este've, L. Lamel, G. J. Jones, T. Sikora. Blip10000: A social video dataset containing spug content for tagging and retrieval[C]. Proceedings of the 4th ACM Multimedia Systems Conference, 2013: 96-101.
14 A. F. Smeaton, P. Over, A. R. Doherty. Video shot boundary detection: Seven years of trecvid activity[J]. Computer Vision and Image Understanding, 2010, 114(4): 411-418.   DOI
15 I. Bieda, A. Kysil, V. Shevchenko. An approach for the scene change localization[C]. Proc. Problems of Decision Making under Uncertainties (PDMU-2021), 2021: 22.
16 E. Apostolidis, V. Mezaris. Fast shot segmentation combining global and local visual descriptors[C]. 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014: 6583-6587.
17 U. Sakarya, Z. Telatar. Video scene detection using dominant sets[C]. 15th IEEE International Conference on Image Processing, 2008: 73-76.
18 Vaibhav Goel, Vaibhav Kumar, Amandeep Singh Jaggi, Preeti Nagrath. Text Extraction from Natural Scene Images using OpenCV and CNN[J]. International Journal of Information Technology and Computer Science(IJITCS), 2019, 11 (9): 48-54.   DOI
19 Md. Arafat Hussain, Emon Kumar Dey. Remote Sensing Image Scene Classification[J]. International Journal of Engineering and Manufacturing(IJEM), 2018, 8 (4): 13-20.   DOI
20 J. Canny. A Computational Approach To Edge Detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8 (6): 679-698.   DOI