Principal motion components for gesture recognition using a single-example
This addresses gesture recognition for applications with limited training data, but it is incremental as it builds on existing PCA and motion analysis techniques.
The paper tackles one-shot gesture recognition with only a single training video per gesture by introducing principal motion components (PMC), which constructs a PCA model from motion energy maps and achieves competitive performance on the ChaLearn Gesture Dataset with over 50,000 gestures.
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.