A Deep Neural Framework To Detect Individual Advertisement (Ad) From Videos

Zongyi Liu; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023, pp. 3578-3587

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 IMDb TV. 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 not only scalable because it uses light weight audio data to do global segmentation, but also robust as the Ad classifier is able to handle different types of contents captured from the popular streaming services such as the IMDb TV, Hulu, CrackleTV, and Prime Video (PV) live sports.