Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Stochastic Tracking of 3D Human Figures Using 2D Image Motion
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Model-Based Hand Tracking Using a Hierarchical Bayesian Filter
IEEE Transactions on Pattern Analysis and Machine Intelligence
Global hand pose estimation by multiple camera ellipse tracking
Machine Vision and Applications
Hand tracking in bimanual movements
Image and Vision Computing
Tracking of human hands and faces through probabilistic fusion of multiple visual cues
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Vision-Based interpretation of hand gestures for remote control of a computer mouse
ECCV'06 Proceedings of the 2006 international conference on Computer Vision in Human-Computer Interaction
Object tracking and segmentation in a closed loop
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
Two people walk into a bar: dynamic multi-party social interaction with a robot agent
Proceedings of the 14th ACM international conference on Multimodal interaction
Robust multi-hypothesis 3D object pose tracking
ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems
Integrating tracking with fine object segmentation
Image and Vision Computing
Visual estimation of pointed targets for robot guidance via fusion of face pose and hand orientation
Computer Vision and Image Understanding
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In this paper we propose a new approach for tracking multiple objects in image sequences. The proposed approach differs from existing ones in important aspects of the representation of the location and the shape of tracked objects and of the uncertainty associated with them. The location and the speed of each object is modeled as a discrete time, linear dynamical system which is tracked using Kalman filtering. Information about the spatial distribution of the pixels of each tracked object is passed on from frame to frame by propagating a set of pixel hypotheses, uniformly sampled from the original object's projection to the target frame using the object's current dynamics, as estimated by the Kalman filter. The density of the propagated pixel hypotheses provides a novel metric that is used to associate image pixels with existing object tracks by taking into account both the shape of each object and the uncertainty associated with its track. The proposed tracking approach has been developed to support face and hand tracking for human-robot interaction. Nevertheless, it is readily applicable to a much broader class of multiple objects tracking problems.