TY - GEN
T1 - Improved object tracking using an adaptive colour model
AU - Chen, Zezhi
AU - Wallace, Andrew M.
PY - 2007
Y1 - 2007
N2 - We present the results of a study to exploit a multiple colour space model (CSM) and variable kernels for object tracking in video sequences. The basis of our work is the mean shift algorithm; for a moving target, we develop a procedure to adaptively change the CSM throughout a video sequence. The optional CSM components are ranked using a similarity distance within an inner (representing the object) and outer (representing the surrounding region) rectangle. Rather than use the standard, Epanechnikov kernel, we have also used a kernel weighted by the normalized Chamfer distance transform to improve the accuracy of target representation and localization, minimising the distance between the two distributions of foreground and background using the Bhattacharya coefficient. To define the target shape in the rectangular window, either regional segmentation or background-difference imaging, dependent on the nature of the video sequence, has been used. Experimental results show the improved tracking capability and versatility of the algorithm in comparison with results using fixed colour models and standard kernels. © Springer-Verlag Berlin Heidelberg 2007.
AB - We present the results of a study to exploit a multiple colour space model (CSM) and variable kernels for object tracking in video sequences. The basis of our work is the mean shift algorithm; for a moving target, we develop a procedure to adaptively change the CSM throughout a video sequence. The optional CSM components are ranked using a similarity distance within an inner (representing the object) and outer (representing the surrounding region) rectangle. Rather than use the standard, Epanechnikov kernel, we have also used a kernel weighted by the normalized Chamfer distance transform to improve the accuracy of target representation and localization, minimising the distance between the two distributions of foreground and background using the Bhattacharya coefficient. To define the target shape in the rectangular window, either regional segmentation or background-difference imaging, dependent on the nature of the video sequence, has been used. Experimental results show the improved tracking capability and versatility of the algorithm in comparison with results using fixed colour models and standard kernels. © Springer-Verlag Berlin Heidelberg 2007.
KW - Adaptive colour space models
KW - Object tracking
KW - Video sequences
UR - http://www.scopus.com/inward/record.url?scp=38149045786&partnerID=8YFLogxK
M3 - Conference contribution
SN - 9783540741954
VL - 4679 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 280
EP - 294
BT - Energy Minimization Methods in Computer Vision and Pattern Recognition - 6th International Conference, EMMCVPR, 2007 Proceedings
T2 - 6th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Y2 - 27 August 2007 through 29 August 2007
ER -