Abstract: Commercials (ads) identification and measure their statistics from a video stream is an essential requirement. The duration of a commercial and the timing on which the commercial runs on TV cost differently to the ads owner. Automatic systems that measure these statistics will facilitate the ad owner. This research presents a system that segment semantic videos and identify commercials automatically from broadcast TV transmission. The proposed technique uses color histogram and SURF features resulting in identify individual ads from TV transmission video stream. Experimental results on unseen videos demonstrate better results for ads identification. The target for the proposed approach is television transmission that do not use blank frame between the ads and a non-ad part of the transmission like in Pakistan, different from European countries TV transmission. The proposed segmentation approach is unsupervised.

Najeed Ahmed Khan, Umair Amin, Waseemullah and Muhammad Umer, “Unsupervised Commercials Identification in Videos” International Journal of Advanced Computer Science and Applications(IJACSA), 8(2), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080217

@article{Khan2017,
title = {Unsupervised Commercials Identification in Videos},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080217},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080217},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {2},
author = {Najeed Ahmed Khan and Umair Amin and Waseemullah and Muhammad Umer}
}