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Video Segmentation Algorithms

Released a comprehensive Python script on our GitHub repository that tackles video segmentation using six different computer vision algorithms. This tool processes any MP4 video file and outputs six distinct segmented versions, each highlighting different aspects of the visual content.

The script implements color-based segmentation that identifies objects by their HSV color ranges, making it particularly effective for scenarios where you need to isolate blue, green, or red objects from the scene. Background subtraction using the MOG2 algorithm excels at detecting moving objects by learning what constitutes the static background over time. Edge detection combines three powerful methods – Canny, Sobel, and Laplacian – to create comprehensive boundary maps of objects and structures within each frame.

K-means clustering groups similar pixel colors into configurable clusters, essentially creating a posterized version of your video that can reveal underlying color patterns. The watershed algorithm treats pixel intensities as topographic elevations, finding natural boundaries between regions through a flooding simulation approach. Optical flow segmentation tracks motion between consecutive frames, highlighting areas of movement and creating dynamic motion maps.

GitHub Repo: https://github.com/akshansh11/Video-Segmentation-Pipeline