Radar-Camera Motion Prediction for Low-Lag Object Tracking
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Solution Overview
Problem
Existing camera systems face challenges in capturing key moments due to limited frames per second, time lag, high power consumption, and imperfect Electronic Image Stabilization (EIS) outcomes, particularly in image-based object tracking and motion prediction.
Innovation Solution
A method utilizing a radio-wave sensor and camera for motion prediction, involving spatial information processing, object tracking, and camera control based on predicted motion trajectories, with frame-level confidence indicators and fusion of spatial and image data for enhanced tracking and stabilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image-based algorithms are used for object tracking, then classification and recognition can be performed, but time lag and high power consumption occur
Solution Approach 1:
The patent replaces image-based algorithms with radar-based spatial information processing. The radar sensor directly detects position and velocity of objects, eliminating the need for complex image processing and machine learning algorithms that cause time lag. This substitution of detection mechanism fundamentally reduces processing time while maintaining tracking accuracy.
Solution Approach 2:
The patent performs preliminary action by continuously acquiring spatial information from the radar sensor before the actual tracking and prediction phases. The radar provides continuous position and velocity data that prepares the system in advance for rapid response, eliminating the time lag inherent in image-based sequential processing.
2Measurement precision
If image-based algorithms are used for object tracking, then classification and recognition can be performed, but power consumption increases
Solution Approach 1:
The patent substitutes computationally intensive image processing algorithms with radar-based detection. The radar sensor provides direct spatial measurements that require significantly less processing power compared to machine learning classification and recognition algorithms, thereby reducing overall system power consumption while maintaining tracking precision.
Solution Approach 2:
The patent extracts only the essential spatial information (position and velocity) from the radar sensor data, eliminating the need for extensive image processing operations. This extraction approach focuses computation only on necessary parameters, reducing power consumption while preserving tracking accuracy.
3Productivity
If camera captures images at limited frames per second, then device complexity is reduced, but key moments cannot be captured
Solution Approach 1:
The patent replaces the camera's frame-rate limited capture mechanism with radar-based continuous spatial monitoring. The radar sensor provides continuous position and velocity data independent of frame rate constraints, enabling reliable detection and prediction of key moments even when the camera operates at lower frame rates or during non-capture periods.
Solution Approach 2:
The radar sensor performs preliminary detection of object positions and velocities continuously, preparing motion trajectory predictions in advance. This preliminary action enables the system to capture key moments reliably by having predictive information ready, overcoming the frame rate limitations of the camera.
4Stability of the object's composition
If Electronic Image Stabilization is performed, then camera movement can be compensated, but hijacking and loss of effective Field of View occur
Solution Approach 1:
The patent replaces image-based EIS algorithms with radar-based motion prediction. The radar sensor detects actual object motion and provides spatial information that enables more accurate prediction of object trajectories. This substitution eliminates the hijacking and FoV loss problems inherent in traditional image-based EIS by using direct spatial measurements rather than image processing.
Solution Approach 2:
The radar sensor performs preliminary detection of object positions and velocities to predict future motion trajectories. This preliminary action enables proactive camera adjustment before EIS issues occur, preventing hijacking and maintaining effective Field of View while achieving image stability.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate and efficient capture of key moments with reduced power consumption, improved EIS, and robust object tracking, overcoming limitations of image-based systems.
Implementation Method 1
receiving spatial information output by a radio-wave sensor, wherein the spatial information includes position and velocity of at least one point
Data Source
AI summary
A method for motion prediction includes receiving spatial information output by a radio-wave sensor, wherein the spatial information includes position and velocity of at least one point; receiving an image captured by a camera; tracking at least one object based on the spatial information and the image to obtain a consolidated tracking result; predicting a motion trajectory of the at least one object based on the consolidated tracking result to obtain a prediction result; and controlling the camera according to the prediction result.