This work proposes a method for an effective and quick monitoring of video contents produced by TV Broadcasters by means of a fully automatic system. The proposed system performs acquisition, recognition and classification of logos labeling video contents hosted by video-sharing platforms. This challenge is addressed in the Laguerre-Gauss wavelet domain; as soon as a logo is located, in any area of the video screen, a detection strategy, based on the analysis of local Fisher information of the selected logo region, is applied. A distance metric on the LG saliency maps, based nearest neighbor algorithm, is defined, to classify the logo in the relevant video portion. A preliminary test on a dataset of 300 heterogeneous videos, produced by several European Broadcasters, was performed, to verify the effectiveness of the proposed method. The experimental results proved the robustness of the implemented logo recognition and classification method, also for video content labeled with different logo sizes and shapes and for video content corrupted by geometric transformations and/or coding degradations.
Mangiatordi Federica, Bernardini Andrea, Pallotti Emiliano, Capodiferro Licia, "Brand detection framework in LG wavelet domain" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Processing: Algorithms and Systems XV, 2017, pp 10 - 15, https://doi.org/10.2352/ISSN.2470-1173.2017.13.IPAS-198