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This paper proposes a lightweight method to attract users and increase viewsof the video by presenting personalized artistic media -- i.e, staticthumbnails and animated GIFs. This method analyzes lightweight thumbnailcontainers (LTC) using computational resources of the client device torecognize personalized events from full-length sports videos. In addition,instead of processing the entire video, small video segments are processed togenerate artistic media. This makes the proposed approach more computationallyefficient compared to the baseline approaches that create artistic media usingthe entire video. The proposed method retrieves and uses thumbnail containersand video segments, which reduces the required transmission bandwidth as wellas the amount of locally stored data used during artistic media generation.When extensive experiments were conducted on the Nvidia Jetson TX2, thecomputational complexity of the proposed method was 3.57 times lower than thatof the SoA method. In the qualitative assessment, GIFs generated using theproposed method received 1.02 higher overall ratings compared to the SoAmethod. To the best of our knowledge, this is the first technique that uses LTCto generate artistic media while providing lightweight and high-performanceservices even on resource-constrained devices.

LTC-GIF: Attracting More Clicks on Feature-length Sports Videos