Exploring Sub-skeleton Trajectories for Interpretable Recognition of Sign Language

• URL: http://arxiv.org/abs/2202.01390v1
• Date: Thu, 3 Feb 2022 03:32:28 GMT
• Title: Exploring Sub-skeleton Trajectories for Interpretable Recognition of Sign Language
• Authors: Joachim Gudmundsson, Martin P. Seybold, John Pfeifer
• Abstract summary: We study the problem of accurately recognizing sign language words. Our method explores a geometric feature space that we call sub-skeleton' aspects of movement. Surprisingly, our simple methods improve sign recognition over recent, state-of-the-art approaches.
• Score: 2.1178416840822027
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in tracking sensors and pose estimation software enable smart systems to use trajectories of skeleton joint locations for supervised learning. We study the problem of accurately recognizing sign language words, which is key to narrowing the communication gap between hard and non-hard of hearing people. Our method explores a geometric feature space that we call `sub-skeleton' aspects of movement. We assess similarity of feature space trajectories using natural, speed invariant distance measures, which enables clear and insightful nearest neighbor classification. The simplicity and scalability of our basic method allows for immediate application in different data domains with little to no parameter tuning. We demonstrate the effectiveness of our basic method, and a boosted variation, with experiments on data from different application domains and tracking technologies. Surprisingly, our simple methods improve sign recognition over recent, state-of-the-art approaches.

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