Body posture and finger pointing are a natural modality for human-machine interaction, but first the system must know what it's seeing.

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This paper introduces "Flocks of Features," a fast tracking method for non-rigid and highly articulated objects such as hands. It combines KLT features and a learned foreground color distribution to facilitate 2D position tracking from a monocular view. The tracker's benefits lie in its speed, its robustness against background noise, and its ability to track objects that undergo arbitrary rotations and vast and rapid deformations. We demonstrate tracker performance on hand tracking with a non-stationary camera in unconstrained indoor and outdoor environments. The tracker yields over threefold improvement over a CamShift tracker in terms of the number of frames tracked before the target was lost, and often more than one order of magnitude improvement in terms of the fractions of particular test sequences tracked successfully.

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The research described in this paper analyzes the in-plane rotational robustness of the Viola-Jones object detection method when used for hand appearance detection. We determine the rotational bounds for training and detection for achieving undiminished performance without an increase in classifier complexity. The result -up to 15°total -differs from the method's performance on faces (30°total). We found that randomly rotating the training data within these bounds allows for detection rates about one order of magnitude better than those trained on strictly aligned data. The implications of the results effect both savings in training costs as well as increased naturalness and comfort of vision-based hand gesture interfaces.