🎯 Human Pose Estimation using Differential Evolution (DE) and MediaPipe BlazePose for Real-Time Optimization
September 2024 / Lugano / Switzerland
Human Pose Estimation (HPS) involves detecting key points of the human body from video or image frames. Traditional methods like CNN-based approaches often suffer from high computational costs and inaccurate results in real-time applications, especially in complex environments such as joint occlusions or noisy data.
🔄 Improvement Over Traditional Methods: In this project, we improve the performance of pose estimation by integrating Differential Evolution (DE) with MediaPipe BlazePose, a state-of-the-art human pose estimation model. This combined approach offers enhanced accuracy, speed, and robustness in real-time applications.
@MediaPipe's BlazePose is a highly efficient and accurate deep learning model developed by Google, specifically designed for real-time human pose estimation. It detects 33 key points of the human body, including shoulders, elbows, knees, and ankles. The model is trained on large datasets and has been optimized for both mobile devices and desktop platforms. BlazePose is ideal for applications such as:
- Fitness tracking 🏋️♂️
- Augmented reality 🌐
- Motion capture 🎮
BlazePose provides the baseline joint positions, which are then optimized further using Differential Evolution (DE) to improve real-time tracking performance.
Real.Time.Differential.Evolution.Human.Pose.Estimation.mp4
@DifferentialEvolution (DE) is a population-based meta-heuristic optimization algorithm that excels in finding global solutions in complex, non-linear spaces. In this project, DE is employed to optimize the detected joint positions in real time, ensuring more accurate and robust pose tracking.
🔧 How DE Works:
- Mutation: DE generates new candidate solutions for joint positions by combining random candidate solutions from the population.
- Crossover: It selects the best solutions by comparing the accuracy of the mutated joint positions with the current ones.
- Selection: The best joint position solution is chosen and applied for each frame of the video.
By continuously refining the joint positions across video frames, DE ensures that human body movements are tracked with high accuracy even in challenging conditions like occlusion or motion blur.
- The input is a video file (e.g.,
test.mp4), which is processed frame by frame. The video is converted to an RGB format for accurate pose detection by BlazePose.
- BlazePose detects 33 key points of the body, providing baseline joint positions. These joints are detected for each frame of the video.
- After BlazePose detects the joint positions, DE optimizes these positions to minimize the error and enhance accuracy.
- DE improves joint detection by reducing noise and smoothing joint movement, providing more accurate pose tracking across frames.
- After joint positions are optimized, the pose is drawn on the original video:
- Annotated Video: Displays the human pose overlaid on the video.
- Skeleton Video: Displays just the skeleton of the detected pose.
- The optimized joint positions are saved in:
- CSV Format: For further analysis of joint movements.
- BVH Format: Commonly used for motion capture and 3D animation systems.
- The videos (annotated and skeleton) are saved as MP4 files for visualization.
- Optimized Joint Detection: DE refines joint positions, reducing error and improving overall accuracy.
- Real-Time Performance: DE ensures faster joint updates, making it ideal for real-time video processing. ⏱️
- Robustness: DE handles occlusion and noisy data better than traditional methods, ensuring smoother tracking in difficult environments.
- Lower Computational Complexity: Compared to deep learning-based methods like CNNs, DE offers lower computational requirements, making it suitable for devices with limited resources.
- Annotated Video: The video with human pose annotations.
- Skeleton Video: The video showing only the skeleton representation.
- CSV and BVH Files: Contain detailed joint position data and are reusable in motion capture and animation software.
Traditional methods like CNN-based Pose Estimation (e.g., OpenPose) provide high accuracy but are computationally expensive, making them less suitable for real-time applications. The Differential Evolution (DE) + BlazePose approach offers a more efficient, lightweight, and real-time solution for human pose tracking. 🏃♂️
- Integration with deep learning models to create hybrid systems combining the strengths of DE and neural networks.
- Multi-person pose estimation using an extended DE framework.
#DifferentialEvolution #HumanPoseEstimation #RealTimeOptimization #BlazePose #MotionCapture #BodyTracking @DifferentialEvolution @MediaPipe