Radio-Wave Sensor Fusion for Camera Motion Prediction
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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 applies preliminary action by using depth sensor data to predict motion trajectories before the actual object movement is fully captured by the camera. The depth sensor detects spatial information in advance, allowing the system to pre-calculate where the object will be, thus eliminating time lag in tracking and capturing key moments.
Solution Approach 2:
The patent introduces an intermediary approach by combining depth sensor data with camera images through a fusion mechanism. The depth sensor acts as an intermediary that provides spatial information about object position and motion, which is then integrated with visual data to achieve more accurate and timely tracking without relying solely on image-based algorithms.
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 applies segmentation by dividing the tracking task into two parts: using the depth sensor to detect spatial information and motion patterns, and using the camera only for capturing images at predicted key moments. This segmentation reduces the computational burden on the camera processing system, thereby lowering power consumption while maintaining tracking accuracy.
Solution Approach 2:
By using depth sensor data for preliminary motion prediction, the system can determine when and where to capture images, rather than continuously processing high-resolution images. This preliminary action reduces the overall power consumption by minimizing the time the camera and its processing units are active at full capacity.
3Use of energy by moving object
If camera captures images at limited frames per second, then power consumption is reduced, but key moments are missed
Solution Approach 1:
The depth sensor performs preliminary detection of motion trajectories and predicts the exact moment when key events will occur. This allows the camera to capture images at the predicted key moments rather than relying on fixed frame rates, ensuring no key moments are missed while maintaining efficient power consumption by capturing only when necessary.
Solution Approach 2:
The system uses feedback from the depth sensor's spatial information to dynamically adjust camera capture timing. The predicted motion trajectories provide feedback that triggers camera captures at optimal moments, ensuring both timely capture of key events and efficient power usage by avoiding unnecessary continuous capturing.
4Stability of the object's composition
If Electronic Image Stabilization is applied, then camera movement can be compensated, but hijacking and loss of focus occur
Solution Approach 1:
The depth sensor acts as an intermediary that provides accurate spatial information about object position and motion, which is used to guide the camera's focus and stabilization. This intermediary input allows the EIS system to distinguish between actual object movement and camera shake, preventing hijacking where the stabilization algorithm incorrectly interprets object motion as camera movement that needs correction.
Solution Approach 2:
The patent replaces traditional image-based EIS mechanisms with a depth-based approach. Instead of analyzing image pixels to determine motion and apply stabilization, the system uses depth sensor data to directly control camera positioning and focus, substituting the mechanical image processing approach with a more reliable physical measurement-based system that avoids the pitfalls of image-based hijacking and focus loss.
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 image quality, and effective EIS, overcoming issues of hijacking, centralization, and loss of focus.
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.