This paper proposes an accurate real-time hand tracking and segmentation algorithm. A particle filter tracks the hands in time, based on colour and motion cues. This filter is able to automatically recover from failures and does not need an initialization phase. The algorithm is proven to be robust against lighting changes, and can be used in unconstrained environments. Hand segmentation is based on a Gaussian Mixture Model and refined using a combination of spatial information. Cues from both HSV and RGB colour space are used to increase robustness
This paper proposes a complete tracking system that is capable of long-term, real-time hand tracking with unsupervised initialization and error recovery. Initialization is steered by a three-stage hand detector, combining spatial and temporal information. Hand hypotheses are generated by a random forest detector in the first stage, whereas a simple linear classifier eliminates false positive detections. Resulting detections are tracked by particle filters that gather temporal statistics in order to make a final decision. The detector is scale and rotation invariant, and can detect hands in any pose in unconstrained environments. The resulting discriminative confidence map is combined with a generative particle filter based observation model to enable robust, long-term hand tracking in real-time. The proposed solution is evaluated using several challenging, publicly available datasets, and is shown to clearly outperform other state of the art object tracking methods.
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