2D Pose Video Based Prediction in Python Projects
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₹800.00
₹4,000.00
2D Pose Video Based Prediction in Python Projects
Abstract
2D pose estimation is a computer vision technique used to detect and track human body keypoints from images or videos. It plays a significant role in applications such as activity recognition, sports analytics, rehabilitation, gesture control, and human–computer interaction. With the help of deep learning models, such as OpenPose, MediaPipe, and HRNet, the process of extracting skeletal joints from video frames has become more accurate and efficient. This project focuses on 2D pose video-based prediction, where input video frames are processed to extract human poses, and these pose sequences are then analyzed for predicting human actions, gestures, or future movements. By leveraging Python frameworks like TensorFlow, PyTorch, and OpenCV, the system ensures high accuracy in real-time environments, making it useful for surveillance, healthcare, and interactive systems.
Existing System
Traditional video analysis systems rely on raw pixel data and handcrafted features, which often fail to capture the structural relationships of human body movements. Earlier approaches to action recognition and prediction used methods like optical flow, trajectory tracking, or feature extraction from silhouettes, which were computationally expensive and highly sensitive to noise, occlusions, and lighting variations. Existing deep learning-based action recognition models perform better but often depend on 3D pose estimation or large video datasets, which are resource-intensive and not suitable for real-time lightweight applications. Furthermore, many existing systems lack the ability to generalize across different environments and individuals, limiting their usability in diverse scenarios.
Proposed System
The proposed system introduces a 2D pose video-based prediction model that extracts skeletal keypoints from each frame of a video using state-of-the-art pose estimation libraries like OpenPose or MediaPipe. Instead of processing full video frames, the system works with pose skeletons, which significantly reduces computational complexity while retaining meaningful motion features. The extracted pose sequences are fed into a prediction model (e.g., LSTM, GRU, or Temporal Convolutional Networks) to learn temporal dependencies and predict human actions or gestures accurately. This lightweight and efficient design allows real-time performance, robustness to background noise, and adaptability across multiple applications such as sports performance tracking, abnormal activity detection, virtual training, and interactive gaming. The system not only enhances prediction accuracy but also ensures scalability and real-time usability compared to traditional methods.
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₹800.00
₹4,000.00 -
₹1,800.00
₹4,500.00 -
₹800.00
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₹1,800.00
₹4,500.00 -
₹800.00
₹4,000.00
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