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Real-time Object Detection and Hand Pose Estimation System for Enhanced User Interaction

saigontechnology.com

Information technology

Challenge

The current lack of a robust, real-time object detection and hand pose estimation system limits the potential for advanced user interfaces and interactive applications. Existing solutions often struggle with inference speed requirements for real-time processing on camera streams. The need exists to automatically identify and track objects and human hand gestures for various applications such as gesture control, human-computer interaction, and augmented reality experiences.

About the Client

A research and development lab specializing in Artificial Intelligence, Computer Vision, and Machine Learning solutions.

Objectives

Develop a real-time object detection system capable of accurately identifying objects within a video stream.
Implement a real-time hand pose estimation system to track hand positions and gestures.
Achieve high inference speed to ensure seamless real-time performance.
Integrate the object detection and hand pose estimation systems for comprehensive analysis.
Create a user-friendly interface for accessing and utilizing the developed system.

Functional Requirements

Real-time face detection and tracking.
Real-time hand detection and tracking.
3D hand keypoint estimation.
Display of detected objects and hand keypoints on the video stream.
Toggle options to enable/disable face and hand models.
Integration with webRTC streaming.

Preferred Technologies

BlazeFace model (for face and hand detection)

Attention Mesh model (for hand pose estimation)

WebRTC streaming

Python

Computer Vision libraries (e.g., OpenCV)

Machine Learning frameworks (e.g., TensorFlow, PyTorch)

Integrations Required

WebRTC API for video stream capture
Potential integration with other AI/ML models in the future

Non-Functional Requirements

Real-time performance (sub-millisecond inference speed)
High accuracy in object detection and hand pose estimation
Scalability to handle multiple video streams
Maintainability and code reusability
Security of video streams and data

Business Impact

This project will enable the development of innovative applications requiring real-time human-computer interaction and object analysis. It will enhance the capabilities of the Saigon Technology AI Research Lab, solidify its expertise in Computer Vision, and potentially lead to the creation of new products and services in areas such as gesture control interfaces, augmented reality, and interactive gaming. The faster inference speeds will be crucial for creating responsive and natural user experiences.