2013/10/17 by Hugo Jair Escalante, Isabelle Guyon, Escalante, Hugo Jair +7
Computer Science · #68T45 #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Hand Gesture Recognition Systems #Human Pose and Action Recognition #Video Analysis and Summarization #cs.CV #msc:68T45
paper · pdf · doi:10.48550/arxiv.1310.4822
openalex publication_date 2013/10/17 · arxiv created 2014/01/31 · arxiv updated 2014/02/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.