2013/12/15 by Jakub Konečný, Konečný, Jakub, Michal Hagara +1
Computer Science · Engineering · #Gait Recognition and Analysis #Hand Gesture Recognition Systems #Human Pose and Action Recognition #cs.CV
paper · pdf · doi:10.48550/arxiv.1312.4190
20 pages, 10 figures, 2 tables To appear in Journal of Machine Learning Research subject to minor revision
arxiv created 2014/02/15 · arxiv updated 2014/02/18
The purpose of this paper is to describe one-shot-learning gesture recognition systems developed on the 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.