Automated Intro Detection ForTV Series

Sammanfattning: Media consumption has shown a tremendous increase in recent years, and with this increase, new audience expectations are put on the features offered by media-streaming services. One of these expectations is the ability to skip redundant content, which most probably is not of interest to the user. In this work, intro sequences which have sufficient length and a high degree of image similarity across all episodes of a show is targeted for detection. A statistical prediction model for classifying video intros based on these features was proposed. The model tries to identify frame similarities across videos from the same show and then filter out incorrect matches. The performance evaluation of the prediction model shows that the proposed solution for unguided predictions had an accuracy of 90.1%, and precision and recall rate of 93.8% and 95.8% respectively.The mean margin of error for a predicted start and end was 1.4 and 2.0 seconds. The performance was even better if the model had prior knowledge of one or more intro sequences from the same TV series confirmed by a human. However, due to dataset limitations the result is inconclusive. The prediction model was integrated into an automated system for processing internet videos available on SVT Play, and included administrative capabilities for correcting invalid predictions.