Video Highlight of Farcana Streaming from Twitch
- 1 Management Department, Farcana, United Arab Emirates
- 2 China Branch of BRICS Institute of Future Networks, Shenzhen, China
- 3 Science Department, Farcana, United Arab Emirates
Abstract
The Highlights are the most interesting, selling moments from video stream, which can make the viewer watch the entire video. They are like a shop window: everything that is bright and colorful goes there. Seeing them, the user can understand in advance what is inside the video. And they are more versatile than trailers: they can be made shorter or longer, embedded in different places in the user interface. The user sees a selection of highlights as soon as he gets to the website or watches a video clip on YouTube or even a section of a stream of a popular blogger/influencer. The user’s attention is immediately attracted by the most memorable shots. Naturally, it is becoming more tedious to manually create all video highlights, due to the immense amount of material that the highlights are needed for. Thus, creating an algorithm, capable of automating the process would make the process significantly simpler. Besides easing up the work, this process will pave the way to a whole set of new applications that before did not seem real. At the same time, this would be a new process where the AI would not need to be fully supervised by a human, but be capable of identifying and labeling the most interesting and attractive moments on screen. After doing a literature review on video highlight detection, this paper has utilized the model presented in the study by [1] to determine the possibility of attaining highlights from the Farcana 2.0 version Twitch video feed.
- Badamdorj, T., Rochan, M., Wang, Y. and Cheng, L. (2022) Contrastive Learning for Unsupervised Video Highlight Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, 18-24 June 2022, 14042-14052. https://doi.org/10.1109/CVPR52688.2022.01365
- Hong, F.T., Huang, X., Li, W.H. and Zheng, W.S. (2020) Mini-Net: Multiple Instance Ranking Network for Video Highlight Detection. European Conference on Computer Vision, Springer, Cham, 345-360. https://doi.org/10.1007/978-3-030-58601-0_21
- Jiao, Y., Li, Z., Huang, S., Yang, X., Liu, B. and Zhang, T. (2018) Three-Dimensional Attention-Based Deep Ranking Model for Video Highlight Detection. IEEE Transactions on Multimedia, 20, 2693-2705. https://doi.org/10.1109/TMM.2018.2815998
- Campbell, C., Sands, S., Ferraro, C., Tsao, H.Y.J. and Mavrommatis, A. (2020) From Data to Action: How Marketers Can Leverage AI. Business Horizons, 63, 227-243. https://doi.org/10.1016/j.bushor.2019.12.002
- Liu, W., Yan, C.C., Liu, J. and Ma, H. (2017) Deep Learning-Based Basketball Video Analysis for Intelligent Arena Application. Multimedia Tools and Applications, 76, 24983-25001. https://doi.org/10.1007/s11042-017-5002-5
- Ye, Q., Shen, X., Gao, Y., Wang, Z., Bi, Q., Li, P. and Yang, G. (2021) Temporal Cue-Guided Video Highlight Detection with Low-Rank Audio-Visual Fusion. Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, 10-17 October 2021, 7950-7959. https://doi.org/10.1109/ICCV48922.2021.00785