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Simplified multitarget tracking using the PHD filter for microscopic video data

Abstract:

The probability hypothesis density (PHD) filter from the theory of random finite sets is a well-known method for multitarget tracking. We present the Gaussian mixture (GM) and improved sequential Monte Carlo implementations of the PHD filter for visual tracking. These implementations are shown to provide advantages over previous PHD filter implementations on visual data by removing complications such as clustering and data association and also having beneficial computational characteristics. ...

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D Wilkinson More by this author
Publication date:
2012
URN:
uuid:6e81495b-a9f8-4f80-afc0-78bb880f2283
Local pid:
oai:eprints.maths.ox.ac.uk:1773

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