Feature tracking in real world scenes (or how to track a cow)

Derek Magee*, Roger Boyle

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (SciVal)


In this paper we present a novel scheme for modelling and tracking complex real life objects. The scheme uses multiple models based on a variation of the Point Distribution Model known as the Vector Distribution Model. Inter and intra-class variation is separated using a variation on Linear Discriminant Analysis known as 'Delta Analysis'. The tracking scheme is stochastic and is based on modelling model characteristics by a set of discrete probability distributions, which are updated in an iterative manner. Initialisation is performed using low level processing and a predictor is used to initialise characteristic probabilities on subsequent frames. This scheme has been applied to the task of tracking livestock in a realistic farmyard situation.

Original languageEnglish
Pages (from-to)5-11
Number of pages7
JournalIEE Colloquium (Digest)
Issue number103
Publication statusPublished - 10 May 1999
Externally publishedYes


Dive into the research topics of 'Feature tracking in real world scenes (or how to track a cow)'. Together they form a unique fingerprint.

Cite this