Robust recognition of logo under makeover in the context of content piracy

Consumption of digital content through medium like mobile phones, television set and laptop has seen dramatic increase over recent years. Possible increase behind this rise of consumption is the availability of internet across different regions and drastic rise in availability of internet bandwidth at a nominal cost. Eventually preparation of digital content to cater various user entertainment taste profile has increased. Since there is an increase in consumption of the digital content, it opens new challenges on securing this content. Visual analytics of the digital content on periodic basis is in rise to keep a check on the security of the content. Broadcast logo availability as part of digital content is unique and tracking this logo is one of the methods to distinguish between original content and pirated content. In recent years there's significant increase in research work carried out on the problems associated with object detection and classification. This implies there is a significant increase in terms of recognition performance of objects of interest. In this paper, the discussion is on different TV broadcast channel logo dataset creation, enhancing this dataset using different data augmentation techniques to extend the logo corpus. Proposed system includes TV broadcast/broadband content logo detection and classification pipeline that demonstrate the application of state-of-the-art object detection algorithm Single shot detector (SSD) for logo detection and classification on TV broadcast channel logo dataset which has undergone significant makeover for representing different logo conditions. Experimental result shows the pipeline potential to robustly recognize the logos under makeover in the context of content piracy