In this project, I explored and compared two popular methods for pose detection: MediaPipe and MoveNet. Through this project, I aimed to analyze the strengths and limitations of each approach and gain insights into their performance in real-time pose estimation. This is initially a project I planned for my lab research but I ended up using a more accurate (and expensive) motion capture system instead of doing pose detection through videos.
MediaPipe:
Using MediaPipe, I implemented a pose detection system that leverages its robust and efficient framework. MediaPipe provides a comprehensive set of pre-trained models and pipelines for various computer vision tasks, including pose estimation. The MediaPipe-based solution offers accurate and reliable pose detection, making it suitable for a wide range of applications.
MoveNet:
Another method I examined is MoveNet, an advanced pose estimation model developed by Google Research. MoveNet utilizes deep learning techniques and lightweight architecture, enabling real-time pose tracking on resource-constrained devices. By implementing MoveNet, I explored its capabilities in delivering accurate and efficient pose estimation results.
Through this project, I conducted a comparative analysis of these two methods, examining factors such as accuracy, speed, and resource requirements. By understanding the trade-offs and unique features of each approach, I aimed to provide insights for selecting the most suitable pose detection method based on specific project requirements.
Overall, this project contributes to the field of computer vision by offering a comprehensive examination of pose detection techniques, showcasing the versatility and performance of MediaPipe and MoveNet in real-world scenarios.