AI Pose Detection System

Real-time Human Pose Tracking & Analysis

Project Overview

Real-time video stream analysis for posture tracking including standing, sitting, sleeping positions. Based on computer vision and machine learning with XTELL Thunder model.

Technical Challenges

Traditional pose detection systems suffer from low accuracy, high latency, and poor robustness, especially in complex backgrounds, occlusion, and lighting variations.

Solution

Adopted lightweight but efficient neural network architecture, combined with multi-scale feature fusion and attention mechanisms to achieve real-time pose detection. Algorithm optimizations include model pruning and quantized inference to ensure smooth operation on embedded devices.

Key Highlights

Detection accuracy up to 95%+

Real-time processing speed 30FPS

Support up to 8 people simultaneous detection

17 human keypoint detection

Low power consumption, runs on mobile devices

Technology Stack

Deep Learning: TensorFlow Lite, PyTorch Mobile

Computer Vision: OpenCV, MediaPipe

Model Optimization: ONNX, TensorRT

Deployment: Android, iOS, Embedded Linux

Hardware Acceleration: GPU, NPU, DSP

Development Timeline

Months 1-2: Requirements analysis and algorithm research

Months 3-4: Model development and training

Month 5: System integration and optimization

Month 6: Testing and deployment