{"id":36276,"date":"2019-10-31T22:10:37","date_gmt":"2019-10-31T19:10:37","guid":{"rendered":"https:\/\/prohoster.info\/blog\/poyavilas-utilita-dlya-udaleniya-dvizhushhihsya-obektov-s-video\/"},"modified":"2019-10-31T22:10:37","modified_gmt":"2019-10-31T19:10:37","slug":"poyavilas-utilita-dlya-udaleniya-dvizhushhihsya-obektov-s-video","status":"publish","type":"post","link":"https:\/\/prohoster.info\/en\/blog\/news\/poyavilas-utilita-dlya-udaleniya-dvizhushhihsya-obektov-s-video","title":{"rendered":"A tool has appeared to remove moving objects from video","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Nowadays, removing unwanted elements from photos is no longer a problem for many. Basic skills in Photoshop or trendy neural networks today can solve the issue. However, when it comes to video, the situation is more complicated, as at least 24 frames need to be processed per second of video.<\/p>\n

\"A<\/p>\n

And here on Github a new feature has emerged<\/a><\/noindex> a utility that automates these actions, allowing the removal of any moving objects from video. You simply need to outline the unnecessary object with the cursor, and the system will do the rest. The utility has a straightforward name \u2014 video-object-removal. However, it is based on advanced technologies.<\/p>\n

The system uses a neural network that processes video frame by frame, replacing the unwanted object or person with the background. The program can change up to 55 frames per second, reconstructing the background based on the surrounding image. Though upon closer inspection, it becomes clear that the object removal method is far from perfect, the results are impressive.<\/p>\n

<\/center><\/p>\n

In some frames, it's visible that a transparent or semi-transparent phantom trace is left in the place of the 'removed' person. The issue is that the system only analyzes the available background and currently doesn't always accurately redraw it. This depends on the complexity of the background \u2014 the simpler and more uniform it is, the better the final result.<\/p>\n

The testing was conducted using Ubuntu 16.04, Python 3.5, Pytorch 0.4.0, CUDA 8.0, with processing done on an NVIDIA GeForce GTX 1080 Ti graphics card. The source materials are open and can be used by anyone. It's worth noting that such technology can also be used for malicious purposes. For example, to 'hide' traffic violations or other crimes captured by cameras.<\/p>\n

<\/p>\n

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Source: 3dnews.ru<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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