Multi-Image Object Correspondence via Visual Signatures
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Solution Overview
Problem
Existing object tracking systems in multi-camera environments are prone to errors due to inaccurate state estimation and rely on prior decisions, leading to poor tracking performance, especially when objects have different appearances across camera views with no prior information.
Innovation Solution
A stateless method that generates similarity scores between visual signatures of candidate objects, incorporating geometric, motion, and visual consistency constraints, to establish correspondence across camera views without relying on specific geometric constraints or prior information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple cameras are used to track objects, then object tracking coverage and detection capability are improved, but establishing correspondence between objects across different camera views becomes more difficult due to appearance variations
Solution Approach 1:
The patent introduces visual signatures as an intermediary representation that mediates between raw image data from multiple cameras and the correspondence establishment process. These visual signatures serve as a common language that allows objects to be identified and matched across different camera views despite appearance variations, effectively resolving the correspondence difficulty while maintaining multi-camera tracking coverage
2Stability of the object's composition
If state-based tracking methods are used to maintain object identity across frames, then tracking continuity is improved, but error propagation occurs over time leading to poor tracking performance
Solution Approach 1:
The patent performs preliminary action by establishing visual signatures and correspondence relationships at the moment objects are first detected or when correspondence is needed, rather than relying on accumulated state information from previous frames. This approach ensures that each correspondence decision is based on current visual evidence rather than potentially erroneous historical states, maintaining tracking continuity while preventing error propagation
3Measurement precision
If prior information about objects is used to establish correspondence, then matching accuracy is improved, but the system cannot handle unknown objects without prior information in the scene
Solution Approach 1:
The patent creates visual signature copies that capture the essential visual characteristics of objects. These signatures serve as portable representations that can be stored in a library for known objects or generated on-the-fly for unknown objects. When establishing correspondence, the system compares visual signatures rather than relying solely on prior information, enabling accurate matching of both known and unknown objects through their visual characteristic profiles
Data Source
AI summary
In one implementation, a method includes generating a set of candidate objects based at least in part on a set of image data, where the set of image data includes image data from a plurality of image sources. The method also includes generating a set of visual signatures, wherein each of the visual signatures in the set of visual signatures characterizes a candidate object in the set of candidate objects. The method further includes transforming at least two candidate objects in the set of candidate objects into a single object according to a determination that correspondence between visual signatures for the at least two candidate objects satisfies one or more correspondence criteria.