Research on Dynamic Gesture Recognition with Low-Pixel ToF-Sensors

Low-pixel time-of-flight (ToF) sensors offer advantages in low power consumption, small size, and low cost. For gesture recognition, there are two advantages. Firstly, they are robust to changes in lighting conditions. Secondly, they could protect privacy. This paper addresses the challenge of utilizing an
$\mathbf{8}\times \mathbf{8}$
pixel ToF sensor with extremely low resolution by proposing a signal processing system. The system includes pre-processing the data through non-global mean filtering to calibrate outliers. Additionally, we design a frames difference sliding window to ensure the integrity of gesture sequences and use Lagrangian interpolation to norm the fixed length. Finally, we design a tiny parallel convolutional network (TPC-Net) to recognize dynamic gestures. Experimental results demonstrate that the proposed system achieves an average accuracy of 98.47% over 6 dynamic gestures. Furthermore, our system achieves better performance with less model complexity compared to existing methods using low-pixel ToF sensors. These results illustrate the effectiveness of our system.