Radar Camera Fusion for Depth-Aware Surveillance
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Solution Overview
Problem
Conventional video surveillance systems lack spatial information such as depths and distances, making it difficult to accurately locate objects in a scene, leading to potential misidentification of locations and increased time to find specific objects.
Innovation Solution
A method combining millimeter-wave radar for 3D point cloud mapping and optical cameras for image capture, with alignment and synchronization of data points and image points using relative and absolute coordinate systems, providing surveillance information with both image and depth, and utilizing smart glasses with IMU for wearer tracking and virtual object placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only optical cameras are used for surveillance, then image quality is good, but spatial information (depth and distance) is lost
Solution Approach 1:
The patent combines millimeter-wave radar and optical camera into a unified surveillance system. The radar provides depth and distance information while the camera provides visual information. Through coordinate system transformation and data fusion, the system merges these different types of information to create comprehensive surveillance data that includes both visual quality and spatial accuracy.
Solution Approach 2:
The patent transitions from 2D image data from cameras to 3D spatial information by integrating radar data. The coordinate system transformation process converts radar's 3D point cloud data into a unified coordinate system that can be correlated with 2D image data, adding the depth dimension to traditional visual surveillance.
2Measurement precision
If multiple sensors are used to obtain spatial information, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent introduces coordinate system transformation as an intermediary process to bridge radar and camera data. By establishing a unified coordinate system and using transformation matrices, the system manages the complexity of integrating multiple sensors through a standardized mathematical framework, making the integration process systematic and manageable.
Solution Approach 2:
The patent creates a universal data processing framework that can handle both radar point cloud data and camera image data through the same coordinate transformation and fusion pipeline. This multi-functional approach allows the system to process different sensor types using unified algorithms, reducing overall system complexity despite using multiple sensors.
3Measurement precision
If radar and camera data are integrated, then spatial accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent performs coordinate system transformation and data alignment in advance, before the actual object detection and localization tasks. By pre-establishing the unified coordinate system and transforming all sensor data into this framework, the system simplifies subsequent processing steps, as the complex transformation work is already completed.
Solution Approach 2:
The patent replaces complex mechanical or manual data alignment processes with mathematical coordinate transformation systems. Using transformation matrices and mathematical models to align radar and camera data eliminates the need for manual calibration and alignment procedures, reducing processing complexity through automated mathematical operations.
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 precise object localization, reduces the chances of operation errors and equipment damage by providing accurate spatial information, allowing users to navigate safely and efficiently to specific targets or objects while avoiding hazardous zones.
Implementation Method 1
producing a steam of data points constituting a 3D point cloud map of a scene through at least a millimeter-wave radar. The 3D point cloud map's stream of data points is obtained by the millimeter-wave radar's transmitting and receiving millimeter wave to and from the scene
Implementation Method 2
detecting the movement, turn, pitch, and the related speeds and angles of a smart glasses by its wearer from an Inertial Measurement Unit (IMU) inside the smart glasses
Data Source
AI summary
An image capturing and depth alignment method includes radar scanning step, image capturing step, translation and synchronization step, alignment step, client detection step, client positioning step, scene map construction step, view image transmitting step, view image processing step, virtual object placement step. through translating, synchronizing, and aligning the radar scanning step's 3D point cloud map and the image capturing step's planar image of the scene, the back-end server therefore obtains surveillance information with both image and depth. Then, through positioning, multiply superimposing, image rotation and matching, speed comparison, uniformization of coordinate systems, and display through the smart glasses, a wearer of the smart glasses may be positioned and tracked in the scene. The wearer may also be instructed to reach a specific target or place of a specific object.