Real-time motion trajectory training and prediction using reservoir computing for intelligent sensing equipment

Area of Science:

• Computer Vision
• Robotics
• Machine Learning

Background:

• Real-time trajectory prediction is crucial for autonomous systems.
• Predicting object motion in 3D space onto a 2D sensing plane presents challenges.

Purpose of the Study:

• To develop and evaluate a reserve network for predicting real-time moving target trajectories.
• To assess the accuracy of future trajectory predictions based on historical data.

Main Methods:

• Utilized historical running trajectory data to train a reserve network model.
• Employed a network trained on 20,000 frames for predicting 1-20 future frames.
• Tested the model's performance on 5,000 frames of trajectory data.

Main Results:

• Achieved prediction errors below 0.01% for 1 future frame.
• Demonstrated prediction errors of 0.8% for 10 future frames.
• Showcased prediction errors of 4% for 20 future frames.

Conclusions:

• The reserve network model shows high accuracy in real-time trajectory prediction.
• The method is effective for predicting object motion in dynamic environments.
• This approach has significant implications for autonomous driving and target tracking.