论文标题
LTC-GIF：吸引更多功能长度体育视频的点击
LTC-GIF: Attracting More Clicks on Feature-length Sports Videos
论文作者
论文摘要
本文提出了一种轻巧的方法，通过介绍个性化的艺术媒体（即静态缩略图和动画gifs）来吸引用户并增加视频的看法。该方法使用客户端设备的计算资源来分析轻巧的缩略图容器（LTC），以识别全长体育视频的个性化事件。此外，处理小型视频片段而不是处理整个视频，以生成艺术媒体。与使用整个视频创建艺术媒体的基线方法相比，提出的方法在计算上更有效。提出的方法检索并使用缩略图容器和视频段，从而减少了所需的传输带宽以及艺术媒体生成期间使用的本地存储数据量。当对Nvidia jetson TX2进行广泛的实验时，该方法的计算复杂性比SOA方法低3.57倍。在定性评估中，与SOA方法相比，使用拟议方法生成的GIF获得了1.02总收视率。据我们所知，这是第一种使用LTC生成艺术媒体的技术，同时即使在资源受限的设备上也提供轻巧和高性能的服务。
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.
