CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Segmentation and Tracking Using Color Mixture Models
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume I - Volume I
Towards Vision-Based 3-D People Tracking in a Smart Room
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
On Importance of Nose for Face Tracking
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
A joint particle filter for audio-visual speaker tracking
ICMI '05 Proceedings of the 7th international conference on Multimodal interfaces
A survey of skin-color modeling and detection methods
Pattern Recognition
Moving object detection by multi-view geometric techniques from a single camera mounted robot
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
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Person tracking is a key requirement for modern service robots. But methods for robot vision have to fulfill several constraints: they have to be robust to errors evoked by noisy sensor data, they have to be able to work under real-world conditions, and they have to be fast and computationally inexpensive. In this paper we present an approach for tracking the position of a person in 3D based on a particle filter. In our framework, each particle represents a hypothesis for the 3D position, velocity and size of the person's head being tracked. Two cameras are used for the evaluation of the particles. The particles are weighted by projecting them onto the camera image and applying a color-based perception model. This model uses skin color cues for face tracking and color histograms for body tracking. In contrast to feature-based approaches, our system even works when the person is temporary or partially occluded.