Binocular Depth Camera Target Localization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing target location techniques are inflexible and offer low accuracy as they require pre-stored feature parameters of the target object, making it difficult to accurately locate targets in dynamic scenes.
Innovation Solution
A system and method that utilize depth images and binocular cameras to determine target coordinates and generate marking images, allowing for flexible and accurate target location by distinguishing potential target objects and interferents in a scene.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pre-stored feature parameters are used for target location, then the locating process is simple, but the locating accuracy is low and the system is inflexible
Solution Approach 1:
The patent transitions from 2D image coordinates to 3D spatial coordinates by introducing depth information through binocular vision. The system calculates real-world coordinates (x, y, z) from image coordinates (u, v) using depth values obtained from stereo matching, enabling accurate spatial localization without requiring pre-stored feature parameters.
Solution Approach 2:
The patent replaces the mechanical approach of pre-storing and matching feature parameters with an optical-mathematical approach. Instead of comparing stored templates with current images, the system uses binocular depth perception and coordinate transformation mathematics to directly calculate target positions, achieving higher accuracy and flexibility.
2Adaptability or versatility
If pre-stored feature parameters are required, then the locating method is straightforward, but it cannot adapt to dynamic scenes
Solution Approach 1:
The patent implements a dynamic locating system that processes images in real-time without relying on static pre-stored parameters. The binocular camera continuously captures depth information, and the system dynamically calculates target coordinates as objects move in the scene, enabling adaptation to dynamic environments while maintaining operational simplicity through automated processing.
Solution Approach 2:
The system performs self-service by automatically acquiring depth information from binocular images, calculating coordinates through mathematical transformation, and identifying targets without human intervention. The automated coordinate transformation and target detection processes eliminate the need for manual parameter storage and matching, simplifying operation while enhancing adaptability.
3Reliability
If feature point identification is used, then the method works for known targets, but it fails to locate unknown or unregistered objects
Solution Approach 1:
The patent creates a universal locating method that can identify any object within the binocular camera's field of view, regardless of whether it is pre-registered or known. The system uses depth-based coordinate transformation that applies to all objects uniformly, enabling both reliable identification of known targets and discovery of unknown targets through their spatial positions.
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 high accuracy and flexibility in locating targets by generating marking images that represent potential target objects, effectively overcoming the limitations of pre-stored feature parameters and improving target identification in dynamic environments.
Implementation Method 1
receive first electronic signals including a first image of the target scene taken by a first image sensor, and receive second electronic signals including a second image of the target scene taken by a second image sensor. The at least one locating device may also be configured to, for each pixel in the first image, determine a horizontal parallax between the pixel of the first image and a corresponding pixel of the second image
Data Source
AI summary
A method and system for locating a target object in a target scene. The method may include obtaining a depth image of the target scene. The depth image may include a plurality of pixels. The method may also include, for each of the plurality of pixels of the depth image, determining a first target coordinate under a target coordinate system. The method may further include generating a marking image according to the depth image and the first target coordinates of the plurality of pixels in the depth image. The marking image may represent potential target objects in the depth image. The method may also include determining a locating coordinate of the target object under the target coordinate system according to the marking image.