Multi-Camera Object Identification Using Expected Location Tracking
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Solution Overview
Problem
Computer vision systems with multiple cameras face high computational burdens in object identification due to the need for visual feature matching across large search spaces and multiple target objects, which is inefficient and resource-intensive.
Innovation Solution
A multi-camera system that uses visual feature matching in one camera's field of view to identify objects and then tracks these objects across different camera views using expected locations, reducing the need for repeated visual feature matching by sharing object identities based on spatial and temporal associations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual feature matching is performed across multiple camera fields of view to identify objects, then object identification accuracy is improved, but computational burden increases significantly
Solution Approach 1:
The system performs visual feature matching only in the first camera's field of view to identify objects, then uses the identified object's expected location to guide searching in subsequent camera fields of view. This preliminary identification action eliminates the need to perform computationally intensive visual feature matching in every camera, thereby reducing overall computational burden while maintaining identification accuracy
Solution Approach 2:
The patent extracts only the necessary information (object identity and expected location) from the first camera's identification result, and uses this extracted information to guide the search in other cameras. This selective extraction approach avoids redundant processing and reduces computational load compared to performing full visual feature matching across all cameras
2Reliability
If visual feature matching is performed in every camera's field of view, then object tracking reliability is improved, but processing time increases
Solution Approach 1:
The system performs preliminary object identification in the first camera before processing other cameras. This preliminary action establishes the object's identity and expected location, allowing subsequent cameras to use this information to efficiently locate and track the same object without repeating the full identification process, thereby reducing processing time while maintaining tracking reliability
Solution Approach 2:
Instead of performing complete visual feature matching in every camera, the system performs partial matching using the expected location information from the first camera. This partial action approach is sufficient for tracking purposes and significantly reduces processing time compared to exhaustive matching in all cameras
Data Source
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AI summary
A system comprising: multiple cameras including at least a first camera having a first field of view and a second camera having a second field of view, wherein the second camera is different to the first camera and the second field of view is different to the first field of view; and identification means for identifying an object captured by one or more of the multiple cameras, wherein the identification means comprises means for: using visual feature matching for a detected object in the first field of view of the first camera to identify the detected object in the first field of view of the first camera as a first object; and using an expected location of the first object in the second field of view of the second camera to identify a detected object in the second field of view as the first object.