The task of recognizing license plates from photographs or videos is extremely relevant nowadays. The development of transport links between cities and countries has led to the need to recognize vehicles from different regions. However, license numbers have their own characteristics for each country, such as differences in symbols, their position and font, which complicate recognition. A universal automatic recognition system must cope with these features. Our previous studies have proven the effectiveness of a model trained specifically for a target country, which for effective recognition requires an additional decision for choosing a country or region. The data used is a dataset of images of license plates mainly from countries that previously belonged to the CIS. We propose a vehicle country recognition model from license plate image based on siamese network model with triplet loss function and negative sampling technique. This model is significantly superior to a solution based on a convolutional network in terms of recognition accuracy, achieving 0.9651 value, as well as time spent on training.
KEYWORDS: Video, Image segmentation, Detection and tracking algorithms, Video processing, 3D modeling, RGB color model, Image processing, Neural networks, Hough transforms, Data modeling
Advertising in TV shows and movies is expensive and has one of the largest market shares in the entire advertising industry. We address the task of adding a given advertising banner in a given video. In this paper, we propose a new algorithm for processing and replacing advertising banners in videos, which preserves the quality of the original video content. This algorithm allows the given posters to be inserted into a video in a fully automated mode. In order to replace a banner, the algorithm requires only a video and an image of the banner to be inserted. Our algorithm uses computer vision methods for localizing banners on the scene, analyzing, and transforming them. We suggest the approach to create a synthetic dataset for fine-tuning advertising banners detection models. We implement three various methods for the banner localization task and compared the approaches with each other and existing methods. The source code and examples of the algorithm performance are publicly available https://github.com/leonfed/ReAds.
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