Context-Aware Object Detection Using Travel Path Metadata
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Solution Overview
Problem
Existing systems for detecting and tracking moving objects on a travel path, such as video tracking systems, face limitations in accuracy due to environmental artifacts and image quality issues like lighting effects, leading to high false alarms and missed detections.
Innovation Solution
A system and method that combines geo-registration information with travel path metadata from global mapping systems to improve the detection of moving objects on a travel path, using an aerial structure like a UAV, which includes an image capturing device, a processor, and a server to process images and reduce false alarms and missed detections by geo-registering images and applying context-aware detection techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If video tracking systems are used to detect moving objects on a travel path, then real-time detection capability is provided, but detection accuracy deteriorates due to environmental artifacts and lighting effects
Solution Approach 1:
The patent introduces travel path metadata as an intermediary element that mediates between the image capture system and the detection algorithm. This metadata includes information about the expected travel path, environmental context, and lighting conditions, which helps the system distinguish between real objects and false detections caused by environmental artifacts and lighting effects.
Solution Approach 2:
The system dynamically adjusts detection parameters based on travel path metadata. By changing parameters such as sensitivity thresholds, detection regions, and matching criteria according to the contextual information from metadata, the system maintains high detection accuracy across varying environmental conditions while preserving real-time capability.
2Reliability
If detection sensitivity is increased to reduce missed detections, then detection coverage improves, but false alarms increase
Solution Approach 1:
The patent applies different detection sensitivities and criteria to different regions based on travel path metadata. Areas along the expected travel path use higher sensitivity to catch moving objects, while areas off the path use lower sensitivity to minimize false alarms. This spatially-varying approach allows the system to maintain both detection coverage and reduce false alarms simultaneously.
Solution Approach 2:
The system uses travel path metadata as feedback information to continuously refine detection parameters. By comparing detected objects against the expected travel path and environmental context from metadata, the system can adjust sensitivity thresholds in real-time to reduce false alarms while maintaining detection coverage for actual moving objects.
3Object-generated harmful factors
If detection sensitivity is decreased to reduce false alarms, then false alarm rate decreases, but missed detections increase
Solution Approach 1:
The patent segments the detection task into multiple stages: first using travel path metadata to identify regions of interest and expected object locations, then applying detection algorithms with optimized sensitivity only in those segmented regions. This segmentation allows the system to maintain low false alarm rates overall while ensuring high detection coverage in areas where moving objects are expected.
Data Source
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AI summary
A system and a method for real-time detection of a moving object on a travel path in a geographical area is provided. The system and method may also be used to track such a moving object in real-time. The system includes an image capturing device for capturing successive images of a geographical area, a geographical reference map comprising contextual information of the geographical area, and a processor configured to calculate differences between successive images to detect, in real-time, a moving object on the travel path. The method includes capturing successive images of the geographical area using the image capturing device, geo-registering at least some of the successive images relative to the geographical reference map, and calculating differences between the successive images to detect, in real-time, an object.