User Engagement Detection-Based Financial Technology Advertising Video Effectiveness Evaluation

Abstract

With the rapid advancement of financial technology, an increasing number of related advertisements have received widespread attention. User engagement detection during the advertisement viewing process directly reflects the effectiveness of the advertising video. Therefore, detecting user engagement during the advertisement viewing process has become a crucial issue. However, traditional engagement detection methods often require significant computational resources, significantly reducing their practicality. To address this issue, the authors propose a method to effectively detect user engagement by fully integrating multiple relatively practical models. Specifically, the authors extract key frame images from user face video and perform super-resolution reconstruction of them. Then image pyramid matching is used to achieve user engagement detection. Finally, the authors establish a reasonable database and conduct sufficient experiments based on it. Experimental results demonstrate that this proposed method has realistic engagement detection accuracy, and the design of multiple steps is also valid.