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A deep neural framework to detect individual advertisement (ad) from videos

By Zongyi (Joe) Liu, Yarong Feng, Yuan Ling, Shunyan Luo, Shujing Dong, Bruce Ferry
2023
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Detecting commercial Ads from a video is important. For example, the commercial break frequency and duration are two metrics to measure the user experience for streaming service providers such as Amazon Freevee. The detection can be done intrusively by intercepting the network traffic and then parsing the service providers data and logs, or non-intrusively by capturing the videos streamed by content providers and then analyzing using the computer vision technologies. In this paper, we present a non-intrusive framework that is able to not only detect an Ad section, but also segment out individual Ads. We show that our algorithm is scalable because it uses light weight audio data to do global segmentation, as well as is domain crossing (movies, TVs and live streaming sports) captured from the popular streaming services such as the Freevee and the Prime Video (PV) live sports.
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