It's all about you: Personalized in-Vehicle Gesture Recognition with a
  Time-of-Flight Camera
Amr Gomaa Guillermo Reyes Michael Feld
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.