Abstract
Segmentation and tracking of objects in video sequences is important for a number of applications. In the supervised variant, segmentation can be achieved by modelling the probability density of image observations taken from an object for use in a Bayesian classifier, and Gaussian mixture models have been applied to this task by several researchers. Motivated by practical difficulties we have experienced with these models we propose a novel and simple alternative approach which combines a strong shape model with histograms of image features and gives good empirical results on test sequences requiring flexible models.
Translated title of the contribution | Supervised Segmentation and Tracking of Non-rigid Objects using a ""Mixture of Histograms"" Model |
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Original language | English |
Title of host publication | Unknown |
Editors | - |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 62 - 65 |
Number of pages | 3 |
Publication status | Published - Oct 2001 |