Target Re-identification via Sensor Fusion and Vector Profiles
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Solution Overview
Problem
Current systems for re-identifying a target object between different view sensors, such as cameras, face challenges in accurately determining and sharing position information, which hinders effective target object tracking and acquisition across various applications.
Innovation Solution
A technique that involves using a first view sensor to determine the position of a target object and passing this information to a second view sensor, enabling the re-identification and acquisition of the target object through vector profiles and image processing, utilizing methods like computer vision and machine learning to account for different viewing angles and perspectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If position information is shared between view sensors, then target object re-identification accuracy is improved, but system complexity increases
Solution Approach 1:
The patent introduces a server as an intermediary component that receives image data from multiple view sensors, performs position determination and vector profile matching, and coordinates the re-identification process. This centralizes the complex computations and data fusion operations, improving re-identification accuracy while managing system complexity through a dedicated intermediary processor rather than distributed complex logic across all sensors
Solution Approach 2:
The system segments the re-identification task into distinct functional components: image data acquisition by view sensors, position determination by the server, vector profile generation and matching by the server, and result coordination by the server. This segmentation allows each component to be optimized independently and simplifies the overall system architecture by dividing the complex re-identification problem into manageable sub-tasks
2Reliability
If multiple view sensors are used for target tracking, then tracking robustness is improved, but information processing time increases
Solution Approach 1:
The patent pre-generates vector profiles from image data obtained by view sensors and stores them in the server before re-identification is needed. This preliminary processing of image data into compact vector profiles significantly reduces the computational burden during actual re-identification operations, enabling faster processing when multiple sensors are involved while maintaining tracking robustness
Solution Approach 2:
The system creates simplified vector profile representations (copies) of target objects from full image data. These vector profiles contain essential geometric and appearance features in a compressed format, allowing rapid comparison and matching across multiple view sensors without processing the complete high-resolution images, thus reducing information processing time while maintaining tracking reliability
Data Source
AI summary
View sensors such as cameras can be used to determine a position of a target object through any number of techniques. In some forms the view sensors can be coupled with a range estimation device such as computer vision to determine the distance of the target object from the view sensor. The position of the target object can take the form of either relative position or absolute position, and can be determined through the distance estimate as well as an angle of the target object from the view sensor. Such angle can be, for example, an azimuth. The estimate of target object position can be used with another image sensor to aid in the re-identification of the target object.