TV Ad Detection Using the Base64 Encoding Technique
Received: 13 July 2021 | Revised: 31 July 2021 | Accepted: 20 August 2021 | Online: 30 August 2021
Corresponding author: Waseemullah
Abstract
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.
Keywords:
TV ads, ad detection, base64Downloads
References
S. Sahel, M. Alsahafi, M. Alghamdi, and T. Alsubait, "Logo Detection Using Deep Learning with Pretrained CNN Models," Engineering, Technology & Applied Science Research, vol. 11, no. 1, pp. 6724-6729, Feb. 2021. https://doi.org/10.48084/etasr.3919
P. Matlani and M. Shrivastava, "An Efficient Algorithm Proposed For Smoke Detection in Video Using Hybrid Feature Selection Techniques," Engineering, Technology & Applied Science Research, vol. 9, no. 2, pp. 3939-3944, Apr. 2019. https://doi.org/10.48084/etasr.2571
C. Colombo, A. D. Bimbo, and P. Pala, "Retrieval of Commercials by Semantic Content: The Semiotic Perspective," Multimedia Tools and Applications, vol. 13, no. 1, pp. 93-118, Jan. 2001. https://doi.org/10.1023/A:1009681324605
N. Dimitrova et al., "Real time commercial detection using MPEG features," in Proceedings of the 9th International Conference on Information Processing and Management of Uncertainty in Knowlwdge-based Systems (IPMU2002), 2002.
X.-S. Hua, L. Lu, and H.-J. Zhang, "Robust learning-based TV commercial detection," in 2005 IEEE International Conference on Multimedia and Expo, Amsterdam, Netherlands, Jul. 2005.
L.-Y. Duan, J. Wang, Y. Zheng, J. S. Jin, H. Lu, and C. Xu, "Segmentation, categorization, and identification of commercial clips from TV streams using multimodal analysis," in Proceedings of the 14th ACM international conference on Multimedia, New York, NY, USA, Oct. 2006, pp. 201-210. https://doi.org/10.1145/1180639.1180697
N. Venkatesh, B. Rajeev, and M. G. Chandra, "Novel TV Commercial Detection in Cookery Program Videos - PDF Free Download," in Proceedings of the World Congress on Engineering and Computer Science 2009, San Francisco, CA, USA, Oct. 2009, vol. 2.
E. El-Khoury, C. Sénac, and P. Joly, "Unsupervised Segmentation Methods of TV Contents," International Journal of Digital Multimedia Broadcasting, vol. 2010, Jun. 2010, Art. no. e539796. https://doi.org/10.1155/2010/539796
Y. Zheng, L. Duan, Q. Tian, and J. S. Jin, "TV Commercial Classification by using Multi-Modal Textual Information," in 2006 IEEE International Conference on Multimedia and Expo, Toronto, Canada, Jul. 2006, pp. 497-500. https://doi.org/10.1109/ICME.2006.262434
Y.-P. Huang, L.-W. Hsu, and F.-E. Sandnes, "An Intelligent Subtitle Detection Model for Locating Television Commercials," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 37, no. 2, pp. 485-492, Apr. 2007. https://doi.org/10.1109/TSMCB.2006.883428
L. Meng, Y. Cai, M. Wang, and Y. Li, "TV Commercial Detection Based on Shot Change and Text Extraction," in 2009 2nd International Congress on Image and Signal Processing, Tianjin, China, Oct. 2009. https://doi.org/10.1109/CISP.2009.5302320
A. Altadmri and A. Ahmed, "A framework for automatic semantic video annotation," Multimedia Tools and Applications, vol. 72, no. 2, pp. 1167-1191, Sep. 2014. https://doi.org/10.1007/s11042-013-1363-6
J. Wang, M. Xu, H. Lu, and I. Burnett, "ActiveAd: A novel framework of linking ad videos to online products," Neurocomputing, vol. 185, pp. 82-92, Apr. 2016. https://doi.org/10.1016/j.neucom.2015.12.038
D. Affi, J. Dumoulin, M. Bertini, E. Mugellini, O. Abou Khaled, and A. Del Bimbo, "SensiTV: Smart EmotioNal System for Impaired People's TV," in Proceedings of the ACM International Conference on Interactive Experiences for TV and Online Video, New York, NY, USA, Jun. 2015, pp. 125-130. https://doi.org/10.1145/2745197.2755512
Z. A. A. Ibrahim, "TV Stream Table of Content: A New Level in the Hierarchical Video Representation," Journal of Computer Sciences and Applications, vol. 7, no. 1, pp. 1-9, Dec. 2018. https://doi.org/10.12691/jcsa-7-1-1
Downloads
How to Cite
License
Copyright (c) 2021 . Waseemullah, M. F. Hyder, M. A. Siddiqui, M. Mukarram
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
