Client-driven Lightweight Method to Generate Artistic Media for Feature-length Sports Videos

Code

• github.com/iamgmujtaba/ltc-gif
  tf★ 2

Abstract

This paper proposes a lightweight methodology to attract users and increase views of videos through personalized artistic media i.e., static thumbnails and animated Graphics Interchange Format (GIF) images. The proposed method analyzes lightweight thumbnail containers (LTC) using computational resources of the client device to recognize personalized events from feature-length sports videos. In addition, instead of processing the entire video, small video segments are used in order to generate artistic media. This makes our approach more computationally efficient compared to existing methods that use the entire video data. Further, 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 that are used during artistic media generation. After conducting experiments on the NVIDIA Jetson TX2, the computational complexity of our method was 3.78 times lower than that of the state-of-the-art 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 on resource-constrained devices.

Tasks

Reproductions