  This paper introduces principal motion components (PMC), a new method for
one-shot gesture recognition. In the considered scenario a single
training-video is available for each gesture to be recognized, which limits the
application of traditional techniques (e.g., HMMs). In PMC, a 2D map of motion
energy is obtained per each pair of consecutive frames in a video. Motion maps
associated to a video are processed to obtain a PCA model, which is used for
recognition under a reconstruction-error approach. The main benefits of the
proposed approach are its simplicity, easiness of implementation, competitive
performance and efficiency. We report experimental results in one-shot gesture
recognition using the ChaLearn Gesture Dataset; a benchmark comprising more
than 50,000 gestures, recorded as both RGB and depth video with a Kinect
camera. Results obtained with PMC are competitive with alternative methods
proposed for the same data set.
