Advanced Machine Learning & Computer Vision
Real-time computer vision systems featuring advanced hand tracking, gesture recognition, and pose estimation algorithms. Implementing cutting-edge machine learning techniques with OpenCV and MediaPipe for high-performance human-computer interaction.
Advanced computer vision systems implementing real-time hand tracking, gesture recognition, and pose estimation algorithms. Demonstrates expertise in machine learning, computer vision, and real-time processing optimization using industry-standard frameworks.
Real-time finger joint detection and tracking using MediaPipe framework with 21-point hand landmark recognition achieving sub-pixel accuracy. Custom gesture recognition algorithms enable intuitive human-computer interaction with 30+ FPS performance on standard hardware.
Built with OpenCV, MediaPipe, and NumPy for advanced image processing. Custom gesture classification models with multi-threading optimization for real-time processing. Achieved 95%+ gesture recognition accuracy across multiple hand positions and lighting conditions with <50ms latency response time.
Gesture recognition accuracy across multiple hand positions and lighting conditions
Consistent processing speed with low-latency response (<50ms)
Sub-pixel accurate hand landmark recognition and tracking