Video Object Tracking Based on a Chamfer Distance Transform

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.