Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Robot Vision
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust analysis of feature spaces: color image segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Computer vision for computer games
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Improved Object Tracking Algorithm Based on New HSV Color Probability Model
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
WOW: wild-open warning for broadcast basketball video based on player trajectory
MM '09 Proceedings of the 17th ACM international conference on Multimedia
BEST: a real-time tracking method for scout robot
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Embodied communication between human and robot in route guidance
Proceedings of the 2007 conference on Human interface: Part I
A robust and adaptive road following algorithm for video image sequence
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
Human limb motion real-time tracking based on camshift for intelligent rehabilitation system
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
On-line feature enhancement for adaptive object tracking
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 1
Online PCA with adaptive subspace method for real-time hand gesture learning and recognition
WSEAS Transactions on Computers
Particle filter-based visual tracking with a first order dynamic model and uncertainty adaptation
Computer Vision and Image Understanding
Proceedings of the 23rd Australian Computer-Human Interaction Conference
IFTrace: Video segmentation of deformable objects using the Image Foresting Transform
Computer Vision and Image Understanding
Pattern Recognition and Image Analysis
Thumb widgets: apply thumb-tracking to enhance capabilities of multi-touch on mobile devices
CHI '13 Extended Abstracts on Human Factors in Computing Systems
Real-time vehicle tracking mechanism with license plate recognition from road images
The Journal of Supercomputing
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The Continuously Adaptive Mean Shift Algorithm (CamShift) is an adaptation of the Mean Shift algorithm for object tracking that is intended as a step towards head and face tracking for a perceptual user interface. In this paper, we review the CamShift Algorithm and extend a default implementation to allow tracking in an arbitrary number and type of feature spaces.In order to compute the new probability that a pixel value belongs to the target model, we weight the multidimensional histogram with a simple monotonically decreasing kernel profile prior to histogram back-projection.We evaluate the effectiveness of this approach by comparing the results with a generic implementation of the Mean Shift algorithm in a quantized feature space of equivalent dimension.The aim if this paper is to examine the effectiveness of the CamShift algorithm as a general-purpose object tracking approach in the case where no assumptions have been made about the target to be tracked.