BMVC 2004, Kingston, 7th-9th Sept, 2004
Multi-Modal Tracking using Texture Changes
C. Kemp and T. Drummond (University of Cambridge)
We present a method for efficiently generating a representation of a multimodal
posterior probability distribution. The technique combines ideas from
RANSAC and particle filtering such that the 3D visual tracking problem can be
partitioned into two levels, while maintaining multiple hypotheses throughout.
A simple texture change-point detector finds multiple hypotheses for the
position of image edgels. From these, multiple locations for each scene edge
are generated. Finally we determine the best pose of the whole structure.
While the multi-modal representation is strongly related to particle filtering
techniques, this approach is driven by data from the image. Hence the resulting
system is able to perform robust visual tracking of all six degrees of
freedom in real time. Real video sequences are used to compare the complete
tracking system to previous systems.