Video object tracking based on a chamfer distance transform

Chen Zezhi, Zsolt L. Husz, Iain Wallace, Andrew M. Wallace

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)


This paper describes the use of variable kernels based on the normalized Chamfer distance transform (NCDT) for mean shift, object tracking in colour video sequences. This replaces the more usual Epanechnikov kernel, improving target representation and localization without increasing the processing time, minimising the distance between successive frame RGB distributions using the Bhattacharya coefficient. The target shape which defines the NCDT is found either by regional segmentation or background-difference imaging, dependent on the nature of the video sequence. The improved performance is demonstrated on a number of colour video sequences. ©2007 IEEE.

Original languageEnglish
Title of host publication2007 IEEE International Conference on Image Processing, ICIP 2007 Proceedings
Publication statusPublished - 2006
Event14th IEEE International Conference on Image Processing 2007 - San Antonio, TX, United States
Duration: 16 Sept 200719 Sept 2007


Conference14th IEEE International Conference on Image Processing 2007
Abbreviated titleICIP 2007
Country/TerritoryUnited States
CitySan Antonio, TX


  • Bhattacharyya coefficient
  • Chamfer distance transform
  • Image segmentation
  • Mean shift
  • Object tracking


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