Scene Change Localization in a Video
DOI:
https://doi.org/10.17721/1812-5409.2021/1.6Keywords:
scene change detection, scene cut, scene break, video analysis, scene localizationAbstract
Millions of videos are uploaded each day to Youtube and similar platforms. One of the many issues that these services face is the extraction of useful metadata. There are a lot of tasks that arise with the processing of videos. For example, putting an ad is better in the middle of a video, and as an advertiser, one would probably prefer to show the ad in between scene cuts, where it would be less intrusive. Another example is when one would like to watch only through the most interesting or important pieces of video recording. In many cases, it is better to have an automatic scene cut detection approach instead of manually labeling thousands of videos. The scene change detection can help to analyze video-stream automatically: which characters appear in which scenes, how they interact and for how long, their relations and importance, and also to track many other issues. The potential solution can rely on different facts: objects appearance, contrast or intensity changed, other colorization, background chang, and also sound changes.
In this work, we propose the method for effective scene change detection, which is based on thresholding, and also fade-in/fade-out scene analysis. It uses computer vision and image analysis approaches to identify the scene cuts. Experiments demonstrate the effectiveness of the proposed scene change detection approach.
Pages of the article in the issue: 57 - 62
Language of the article: Ukrainian
References
REDDY, B., JADHAV, A. (2015): Comparison of Scene Change Detection Algorithms for Videos, 2015 Fifth International Conference on Advanced Computing & Communication Technologies (ACCT), Haryana, India, pp. 84-89.
ROTMAN, D., PORAT, D., ASHOUR, G. (2016): Robust and Efficient Video Scene Detection Using Optimal Sequential Grouping, 2016 IEEE International Symposium on Multimedia (ISM), pp. 275-280.
ROTMAN, D., PORAT, D., ASHOUR, G. (2017): Robust video scene detection using multimodal fusion of optimally grouped features, 2017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP), pp. 1-6.
LI, LI, ZENG, X., LI, XI, HU, W., ZHU, P. (2009): Video shot segmentation using graphbased dominant-set clustering, Proceedings of the First International Conference on Internet Multimedia Computing and Service (ICIMCS '09), Association for Computing Machinery, New York, NY, USA, pp. 166–169.
ZABIH, R., MILLER, J., MAI, K. (1995): A feature-based algorithm for detecting and classifying scene breaks, Proceedings of the third ACM international conference on Multimedia (MULTIMEDIA '95), Association for Computing Machinery, New York, NY, USA, pp. 189–200.
SZE, K.-W., LAN, K.-M., QIU, G. (2003): Scene cut detection using the colored pattern appearance model, Proceed. 2003 International Conference on Image Processing, pp. II-1017.
KRULIKOVSKÁ, L., POLEC, J., HIRNER, T. (2012): Fast Algorithm of Shot Cut Detection, World Academy of Science, Engineering and Technology, Open Science Index 67, Internatinal Journal of Electronics and Communication Engineering, 6 (7), pp. 633-636.
CANNY, J.,(1986):A Computational Approach to Edge Detection, IEEE Trans. on Pattern Analysis and Machine Intelligence, 8 (6), pp. 679-698.
PySceneDetect documentation [Online] – Resource Access mode: https://pyscenedetect.readthedocs.io/
Downloads
Published
How to Cite
Issue
Section
License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).