lim-anggun/FgSegNet
FgSegNet: Foreground Segmentation Network, Foreground Segmentation Using Convolutional Neural Networks for Multiscale Feature Encoding
This project helps operations engineers or security analysts automatically detect moving objects in video feeds. It takes raw video footage as input and outputs a clear segmentation of the foreground (moving objects) from the static background, highlighting what has changed in the scene. This is useful for anyone who needs to monitor changes in a fixed visual environment.
248 stars. No commits in the last 6 months.
Use this if you need to reliably identify and isolate moving elements within surveillance footage or other fixed-camera video streams.
Not ideal if you need to track objects across multiple cameras or identify specific types of objects (e.g., 'person' vs. 'vehicle').
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Last pushed
Jan 26, 2019
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