  The purpose of this paper is to describe one-shot-learning gesture
recognition systems developed on the \textit{ChaLearn Gesture Dataset}. We use
RGB and depth images and combine appearance (Histograms of Oriented Gradients)
and motion descriptors (Histogram of Optical Flow) for parallel temporal
segmentation and recognition. The Quadratic-Chi distance family is used to
measure differences between histograms to capture cross-bin relationships. We
also propose a new algorithm for trimming videos --- to remove all the
unimportant frames from videos. We present two methods that use combination of
HOG-HOF descriptors together with variants of Dynamic Time Warping technique.
Both methods outperform other published methods and help narrow down the gap
between human performance and algorithms on this task. The code has been made
publicly available in the MLOSS repository.
