Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic Programming for Detecting, Tracking, and Matching Deformable Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Detection in Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Computer Vision through Kernel Density Estimation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Skin-Color Modeling and Adaptation
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume II
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation and Tracking of Faces in Color Images
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Comparison of Five Color Models in Skin Pixel Classification
RATFG-RTS '99 Proceedings of the International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems
Adaptive Color Space Switching for Face Tracking in Multi-Colored Lighting Environments
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Regression based Bandwidth Selection for Segmentation using Parzen Windows
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Facial asymmetry quantification for expression invariant human identification
Computer Vision and Image Understanding - Special issue on Face recognition
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
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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.