Real-time Hand Movement Trajectory Tracking with Deep Learning

In this study, we employed deep learning to develop a real-time hand trajectory tracking system. Our primary approach integrates the MobileNetv2 single-shot multibox detector, known for accuracy, with the versatile CAMShift algorithm. This synergy ensures robust hand detection across diverse scenarios. Through rigorous testing on webcam images and leveraging advanced feature extraction methods, such as contour discernment and skin hue differentiation, we report an 88.17% increase in detection accuracy over traditional models. Moreover, with a latency of merely 0.0343 s, our system demonstrates its prowess in immersive gaming and assistive devices for individuals with disabilities