42 lines
1.2 KiB
Markdown
42 lines
1.2 KiB
Markdown
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# Challenges
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- Clutter
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- Distractors
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- Occlusion
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- Hidden by other objects
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- Objects appearance can evolve
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- Rotation, scale, camera viewpoint
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- Take new template every n frames
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- Take new template when confidence falls below threshold
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# Background Tracking
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- Static camera
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- Capture clean shots of background
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- Object present
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- Average enough footage
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- Background image - current video frame = difference image
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- Threshold for binary mask
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# Nearest Neighbour Tracking
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- Decide component with closest centroid using previous centroid
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- Not good for occlusion
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- Will snap to next candidate
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# Blob Tracking
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- Build colour model of object
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- Eigenmodel
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- Mask of pixels that match object
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- Use centroid as location over time
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- Pick connected component with centroid closest to previous location
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- Good for distinctive colours
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- Not for practical situations though
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# Template Tracking
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- Sample distinctive patch from image
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- Search all positions in video for patch
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- Use cross-correlation
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- Illumination changes
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- Brightness is uniform shift of greyscale values up or down
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- Correlated to the mean pixel value
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- Subtract means in template and frame to give invariance
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- Normalised cross-correlation
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