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We organized a challenge on gesture recognition: http://gesture.chalearn.org. We made available a large database of 50,000 hand and arm gestures videorecorded with a Kinect T M camera providing both RGB and depth images. We used the Kaggle platform to automate submissions and entry evaluation. The focus of the challenge is on " one-shot-learning " , which(More)
The Kinect T M camera has revolutionized the field of computer vision by making available low cost 3D cameras recording both RGB and depth data, using a structured light infrared sensor. We recorded and made available a large database of 50,000 hand and arm gestures. With these data, we organized a challenge emphasizing the problem of learning from very few(More)
This paper describes the data used in the ChaLearn gesture challenges that took place in 2011/2012, whose results were discussed at the CVPR 2012 and ICPR 2012 conferences. The task can be described as: user-dependent, small vocabulary, fixed camera, one-shot-learning. The data include 54,000 hand and arm gestures recorded with an RGB-D $$\hbox(More)
The purpose of this paper is twofold. First, we introduce our <i>Microsoft Kinect</i>--based video dataset of American Sign Language (ASL) signs designed for body part detection and tracking research. This dataset allows researchers to experiment with using more than 2-dimensional (2D) color video information in gesture recognition projects, as it gives(More)
—Rheumatoid Arthritis (RA) is a chronic disease that leads to inflammation of joints and the surrounding tissues and is a major cause of reduced quality of life and disability. RA can also cause major organ damage and is an independent risk factor for cardiovascular disease. Physical Therapy (PT) and Physical Activity (PA) have been shown to mitigate the(More)
In this paper, we propose a novel approach called class-specific maximization of mutual information (CSMMI) using a submodular method, which aims at learning a compact and discriminative dictionary for each class. Unlike traditional dictionary-based algorithms, which typically learn a shared dictionary for all of the classes, we unify the intraclass and(More)
Recognizing sign language is a very challenging task in computer vision. One of the more popular approaches, Dynamic Time Warping (DTW), utilizes hand trajectory information to compare a query sign with those in a database of examples. In this work, we conducted an American Sign Language (ASL) recognition experiment on Kinect sign data using DTW for sign(More)
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(More)