Home
AI-based advertisement recognition is a cutting-edge technology that utilizes visual information to identify and track specific ads within multiple video streams for the purpose of auditing by marketing and brand agencies. By analyzing visual elements, such as product images, color schemes, and distinctive editing styles, the AI system can detect and track the presence of a given sample advertisement within various video streams.
This technology offers several benefits to marketing and brand agencies:
AI-driven advertisement recognition enables agencies to track the frequency and reach of their advertisements across multiple channels, providing valuable insights into the ad's performance and overall impact.
By identifying and tracking competitors' ads, agencies can gain a better understanding of their market position and adjust their strategies accordingly.
AI-based advertisement recognition can help ensure that ads are not being displayed alongside inappropriate or conflicting content, maintaining brand integrity and reputation.
By understanding the context in which an advertisement appears, agencies can optimize their advertisement placement, ensuring they reach the intended audience with relevant content.
With AI-driven advertisement recognition, marketing and brand agencies can access real-time data on advertisement performance, allowing them to make informed decisions and quickly adapt their strategies as needed.
In summary, AI-based advertisement recognition using visual information offers marketing and brand agencies a powerful tool for auditing and optimizing their advertising campaigns. By tracking ads in multiple video streams, these agencies can gain valuable insights into ad performance, competition, and overall market dynamics, ultimately leading to more effective and targeted advertising strategies.
The subject repository is responsible for Brand Recognition. Final classification is implemented as server-client architecture. Scenes under consideration are segregated using features extraction and matching using deep learning tools. This system can be applied on multiple streams for identification of different scenes, already available in feature store. Simply explained, if a scene to be tracked is available, it can be identified on live stream.
The model used in this architecture is ResNet-18, on Pytorch framework.
