Challenges

• Clutter

  • Distractors

• Occlusion

  • Hidden by other objects

• Objects appearance can evolve

  • Rotation, scale, camera viewpoint

• Take new template every n frames

• Take new template when confidence falls below threshold

Background Tracking

• Static camera
  • Capture clean shots of background
• Object present
  • Average enough footage
• Background image - current video frame = difference image
  • Threshold for binary mask

Nearest Neighbour Tracking

• Decide component with closest centroid using previous centroid
• Not good for occlusion
  • Will snap to next candidate

Blob Tracking

• Build colour model of object
  • Eigenmodel
• Mask of pixels that match object
• Use centroid as location over time
• Pick connected component with centroid closest to previous location
• Good for distinctive colours
  • Not for practical situations though

Template Tracking

• Sample distinctive patch from image
• Search all positions in video for patch
• Use cross-correlation
• Illumination changes
  • Brightness is uniform shift of greyscale values up or down
  • Correlated to the mean pixel value
  • Subtract means in template and frame to give invariance
  • Normalised cross-correlation