We present an approach to detecting and recognizing gestures in a stream of multi-modal data. Our approach combines a slidingwindow gesture detector with features drawn from skeleton data, color imagery, and depth data produced by a first-generation Kinect sensor. The detector consists of a set of one-versus-all boosted classifiers, each tuned to a specific gesture. Features are extracted at multiple temporal scales, and include descriptive statistics of normalized skeleton joint positions, angles, and velocities, as well as image-based hand descriptors. The full set of gesture detectors may be trained in under two hours on a single machine, and is extremely efficient at runtime, operating at 1700fps using only skeletal data, or at 100fps using fused skeleton and image features. Our method achieved a Jaccard Index score of 0.834 on the ChaLearn-2014 Gesture Recognition Test dataset, and was ranked 2nd overall in the competition.

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