# Automatic TV Logo Identification for Advertisement Detection without Prior Data
## 2 Literature Review
It divides the detection into two different workflows: one for detection and removal of opaque and semitransparent logos, and the other for animated logos. The first workflow estimates the position of the logo by computing an average gradient map over several frames. For the animated logos, the image is divided into a grid and, for each block, the period of change and its associated quality are estimated. These metrics are then used to identify the period of animation and location of the animated logo. However, the only results reported are related to the quality of the image after the logo removal, without any mention on the actual logo detection capabilities nor datasets used. This hinders the perception of how well the algorithm actually performs when applied exclusively to logo detection. Histogram of Oriented Gradients (HOG) [13] has been used in many object detection problems, due to the quality of the extracted features, including application in logo detection problems. For instance, in [14] spatial information is coupled with HOG to describe the channel logos of a database in order to define a library of template logos. The logo detection is then accomplished by comparing the extracted features of each input frame with the logo database. A similar method based on the usage of color segmentation has been proposed in [15] that first applies a low-cost color segmentation to obtain candidate logo locations, that are then processed by an SVM trained with HOG features obtained from a logo dataset. However, a comparison with state-of-the-art methods is not reported nor does it present details of the dataset used. Note that the usage of color in the context of advertisement detection may not always be straightforward, as there are multiple color spaces and possible metrics [16] that may convey different information. The difficult task of logo detection is made harder due to inter-class similarity and intra-class difference in the logo images [17]. Currently, deep learning has demonstrated impressive performances in object detection, with many object detection methods being proposed, including Faster R-CNN [18], YOLO [19], and SSD [20], but at the cost of large amounts of data. More recently, anchor-free detection methods [21; 22] have achieved better performance compared to two- and one-stage anchor-based detection methods, but they also require extensive amounts of training data. Deep learning-based object detection methods have been used in logo detection, but without focusing on the case of broadcasting content. [PERSON] et al. [17] trained a detector with logo and non-logo background to retrieve logo images. [PERSON] et al. [23; 24] studied the effect of data augmentation by creating synthesized logo images for model learning. In [PERSON] et al. [25], the authors identified the most compatible training images with logo examples from a noisy image-level dataset to iteratively trained a model. [PERSON] et al. [26] proposed the LogoNet architecture that includes a spatial attention module and hourglass-like feature extraction backbone, and while reported experiments show approximately 1.5% improvement in performance compared to state-of-the-art anchor-free detection network on FlickrLogos-32 dataset, the proposed approach requires knowledge of the possible logos and does not target broadcasting content. A deep learning weakly supervised approach for logo detection in broadcasting is presented in [27]. The proposed system combines two different networks: a Region Proposal Network (RPN) and a Fast R-CNN network [18]. The RPN returns positive and negative sample proposals for logo location. However, it requires manual selection of the positive samples for the training. This network is then used to extract positive and negative samples of logos that are annotated and fed to the Fast RCNN to train the detector. Although this approach obtains good results, it requires manual annotation of the logos.