Video Segmentation using UNET in TensorFlow 2.0 (Keras) | UNET Segmentation | Deep Learning

Video Segmentation using UNET in TensorFlow 2.0 (Keras) | UNET Segmentation | Deep Learning

In this video, we are going to use the UNET model that is trained on the Person Segmentation dataset for video segmentation.

In this video, we are going to use the UNET model that is trained on the Person Segmentation dataset for video segmentation.

In this tutorial, we are going to extract each frame from a video, process them and give them to the UNET. The UNET gives us the mask, using those mask, we extract the segmented part. Later, we join all the segmented part frame to form a video.

The UNET is built for Biomedical Image Segmentation. It is the base model for any segmentation task. It follows an encoder-decoder approach. It used skip connection to get the local information during downsampling path and use it during the upsampling path.

CODE: https://github.com/nikhilroxtomar/Unet-for-Person-Segmentation

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