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|
|Title of host publication||Unknown|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Pages||62 - 65|
|Number of pages||3|
|Publication status||Published - Oct 2001|