Fixed Camera and Drone Collaboration for Uncertain Object Identification
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Solution Overview
Problem
Existing systems face challenges in making decisions regarding object recognition when the certainty level of image matches is intermediate, between 10% and 90%, as it is difficult to determine if an image-captured object matches an object of interest with sufficient confidence.
Innovation Solution
The implementation of a method and system that involves collaboration between fixed cameras and camera-equipped unmanned mobile vehicles, where the fixed camera captures an initial image and, if the certainty level is within a predetermined range, dispatches the unmanned vehicle to obtain a secondary view of the object, enhancing the recognition certainty through intelligent dispatch and coordination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed camera captures an image and determines object match with intermediate certainty level (10-90%), then the system can identify potential objects of interest, but it becomes difficult to make corresponding decisions based on such intermediate levels of certainty
Solution Approach 1:
The system dynamically adjusts the certainty threshold for decision-making. When a fixed camera detects an object with intermediate certainty (10-90%), the system automatically transitions from a static decision rule to a dynamic response: dispatching an unmanned mobile device to capture additional images. This dynamic adjustment resolves the contradiction by making the decision-making process adaptive rather than rigid, allowing the system to handle intermediate certainty cases effectively.
Solution Approach 2:
The system introduces an intermediary component - an unmanned mobile device with camera - to resolve the uncertainty. When the fixed camera's certainty level falls in the intermediate range, the intermediary mobile device is dispatched to capture additional images from different positions or angles. This intermediary action provides supplementary information that enables a more confident final decision, thereby resolving the difficulty of making decisions based on intermediate certainty levels.
2Measurement precision
If the system dispatches an unmanned mobile vehicle to obtain a secondary view when certainty is intermediate, then object recognition certainty is improved, but system complexity and resource consumption increase
Solution Approach 1:
The system segments the object recognition task between two types of devices with complementary roles: fixed cameras for initial detection and unmanned mobile devices for verification. This segmentation allows each component to specialize in its strength - fixed cameras provide wide coverage and initial identification, while mobile devices provide mobile verification capabilities. The segmentation reduces overall system complexity by creating modular, independent units with clear division of labor.
Solution Approach 2:
The system implements self-service through automated decision-making and dispatch algorithms. When the fixed camera detects an object with intermediate certainty, the system automatically determines the need for additional verification and dispatches the appropriate unmanned mobile device without human intervention. The system self-manages the coordination between fixed and mobile devices, reducing operational complexity and enabling scalable deployment.
3Device complexity
If the system uses only fixed cameras for object identification, then system simplicity is maintained, but the ability to improve identification certainty through multiple viewpoints is limited
Solution Approach 1:
The system achieves multi-functionality by having unmanned mobile devices serve dual purposes: they can perform verification tasks when dispatched by fixed cameras, and they can also independently conduct surveillance or investigation missions. This universality justifies the added complexity by providing multiple operational modes - the mobile devices are not merely supplementary but can operate autonomously, thereby improving identification certainty while maintaining operational flexibility.
Data Source
AI summary
A process for fixed camera and unmanned mobile device collaboration is disclosed in order to improve identification of an object of interest. A first point of view (POV) of a captured object is obtained and it is determined, with a first level of certainty, that the captured first POV of the object matches a stored object of interest. Then one or more camera-equipped unmanned mobile vehicles are identified in a determined direction of travel of the first captured object, and a dispatch instruction and intercept information is then transmitted to the one or more camera-equipped unmanned mobile vehicles. Subsequently, a captured second POV of the first captured object is received via the one or more camera-equipped unmanned mobile vehicles. The captured second POV of the captured object is used to determine, with a second level of certainty, that the captured object matches the stored object of interest.