Open AccessTV Ad Detection Using the Base64 Encoding Technique
Author(s) -
. Waseemullah,
Muhammad Faraz Hyder,
Mubashir Ali Siddiqui,
M. Mukarram
Publication year - 2021
Publication title -
engineering, technology and applied science research/engineering, technology and applied science research
Language(s) - English
Resource type - Journals
eISSN - 2241-4487
pISSN - 1792-8036
DOI - 10.48084/etasr.4337
Subject(s) - computer science , python (programming language) , string (physics) , task (project management) , encoding (memory) , artificial intelligence , computer vision , speech recognition , computer graphics (images) , operating system , mathematics , engineering , systems engineering , mathematical physics
Automatic TV ad detection is a challenging task in computer vision. Manual ad detection is considered a tedious job. Detecting advertisements automatically saves time and human effort. In this paper, a method is proposed for detecting repeated video segments automatically, since generally, ads appear in TV transmissions frequently. At first, the user is allowed to browse the advertisements needed to be detected, and the video in which they are to be detected. The videos are then converted into a text file using the Base64 encodings. In the third step, the advertisements are detected using string comparison methods. In the end, a report, with the names of the advertisements is shown against the total time and the number of times these advertisements appeared in the stream. The implementation was carried out in python.