Above-Horizon Target Tracking Using Horizon-Based Image Segmentation
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Solution Overview
Problem
Conventional methods for above-horizon target tracking face challenges in accurately detecting and tracking objects beyond the horizon, such as clouds or ground data, which can lead to difficulties in air collision avoidance for aircraft.
Innovation Solution
A system comprising an imaging sensor coupled to a robot, an imaging module to capture images, an image-processing module to identify objects above the horizon, and a tracking module to determine the object's trajectory relative to the robot, utilizing algorithms like Otsu thresholding and morphological operations to segment and highlight features in the sky region.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image processing methods are used for above-horizon target tracking, then the system can detect objects in the scene, but it cannot accurately distinguish objects above the horizon from other objects like clouds or ground data
Solution Approach 1:
The patent applies segmentation by dividing the image into sky region and ground region based on the horizon line. The image processing module segments the captured image to identify and separate objects above the horizon from clouds and ground data, enabling accurate detection of above-horizon targets by focusing analysis only on the relevant sky portion of the image.
Solution Approach 2:
The patent extracts the horizon line from the captured image as a key reference feature. By detecting and extracting the horizon position, the system creates a clear boundary between sky and ground regions, allowing it to isolate and track objects above the horizon while filtering out irrelevant objects like clouds below the horizon or ground data.
2Reliability
If the system tracks objects above the horizon, then it can provide collision avoidance data, but it may mistakenly identify clouds or ground data as targets
Solution Approach 1:
The patent uses segmentation to divide the image into sky and ground regions using the horizon line as a separator. This spatial segmentation allows the system to reliably identify objects above the horizon as potential targets while automatically excluding clouds below the horizon and ground data, thereby reducing false target identification and improving collision avoidance reliability.
Solution Approach 2:
The horizon line acts as an intermediary reference that mediates between the captured image and target identification. By using the detected horizon position as an intermediate reference feature, the system can accurately distinguish above-horizon objects from below-horizon objects, preventing false identification of clouds or ground data as aerial targets.
Data Source
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AI summary
Disclosed herein is a system that comprises an imaging sensor coupled to a robot. The system also comprises an imaging module configured to capture images of a scene within a field of view of the imaging sensor, which is coupled to the robot. The system further comprises an image-processing module configured to process the captured images to identify an object above a horizon in the scene within the field of view of the imaging sensor. The system additionally comprises a tracking module configured to track a trajectory of the object based on the captured images to determine positions of the object relative to the robot.