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arXiv · 2310.01659

It's all about you: Personalized in-Vehicle Gesture Recognition with a Time-of-Flight Camera

Amr Gomaa·Guillermo Reyes·Michael Feld
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Abstract

Despite significant advances in gesture recognition technology, recognizing gestures in a driving environment remains challenging due to limited and costly data and its dynamic, ever-changing nature. In this work, we propose a model-adaptation approach to personalize the training of a CNNLSTM model and improve recognition accuracy while reducing data requirements. Our approach contributes to the field of dynamic hand gesture recognition while driving by providing a more efficient and accurate method that can be customized for individual users, ultimately enhancing the safety and convenience of in-vehicle interactions, as well as driver's experience and system trust. We incorporate hardware enhancement using a time-of-flight camera and algorithmic enhancement through data augmentation, personalized adaptation, and incremental learning techniques. We evaluate the performance of our approach in terms of recognition accuracy, achieving up to 90\%, and show the effectiveness of personalized adaptation and incremental learning for a user-centered design.

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BibTeXRIS

Amr Gomaa, Guillermo Reyes, Michael Feld. 2023-10-02. It's all about you: Personalized in-Vehicle Gesture Recognition with a Time-of-Flight Camera. https://doi.org/10.1145/3580585.3607153

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