LTC-GIF: Attracting More Clicks on Feature-length Sports Videos
This work addresses the challenge of increasing video views for sports content providers by offering a more efficient and effective way to create engaging previews, though it is incremental as it builds on existing artistic media generation techniques.
The paper tackles the problem of attracting more clicks on feature-length sports videos by proposing a lightweight method that generates personalized artistic media like thumbnails and GIFs, resulting in 3.57 times lower computational complexity and 1.02 higher user ratings compared to the state-of-the-art.
This paper proposes a lightweight method to attract users and increase views of the video by presenting personalized artistic media -- i.e, static thumbnails and animated GIFs. This method analyzes lightweight thumbnail containers (LTC) using computational resources of the client device to recognize personalized events from full-length sports videos. In addition, instead of processing the entire video, small video segments are processed to generate artistic media. This makes the proposed approach more computationally efficient compared to the baseline approaches that create artistic media using the entire video. The proposed method retrieves and uses thumbnail containers and video segments, which reduces the required transmission bandwidth as well as the amount of locally stored data used during artistic media generation. When extensive experiments were conducted on the Nvidia Jetson TX2, the computational complexity of the proposed method was 3.57 times lower than that of the SoA method. In the qualitative assessment, GIFs generated using the proposed method received 1.02 higher overall ratings compared to the SoA method. To the best of our knowledge, this is the first technique that uses LTC to generate artistic media while providing lightweight and high-performance services even on resource-constrained devices.