Cross-Camera Obstacle Tracking With Global ID Fusion
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Solution Overview
Problem
In autonomous driving systems, the limited processing capacity of vehicles leads to image information loss and reduced tracking accuracy due to the inability to effectively fuse obstacle tracking data from multiple cameras, resulting in potential driving control errors.
Innovation Solution
A cross-camera obstacle tracking method that establishes a mapping relation between local and global identifiers for obstacles across multiple cameras, determining similarities to fuse tracking results and improve accuracy, ensuring key obstacle tracking data is not lost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If obstacle tracking data from multiple cameras is processed independently without fusion, then processing capacity requirements are reduced, but tracking accuracy deteriorates due to information loss
Solution Approach 1:
The system segments the obstacle tracking process into independent camera-level processing units, where each camera processes its own tracking data separately. This segmentation allows parallel processing that reduces the processing burden on any single unit while maintaining the capability for subsequent fusion of results to achieve high tracking accuracy.
Solution Approach 2:
The system merges obstacle tracking results from multiple cameras through identifier mapping and data fusion. By establishing mapping relationships between local identifiers from different cameras and global identifiers, the system combines tracking data to improve accuracy while managing processing capacity through structured integration.
2Measurement precision
If obstacle tracking data from multiple cameras is fused, then tracking accuracy is improved, but processing complexity increases
Solution Approach 1:
The system introduces global identifiers as an intermediary mechanism to facilitate data fusion between multiple cameras. The global identifier acts as a mediator that maps and connects local identifiers from different camera systems, enabling accurate matching and fusion of obstacle tracking data without requiring complex direct multi-camera coordination.
Solution Approach 2:
The system performs preliminary establishment of mapping relationships between local and global identifiers before actual obstacle tracking and fusion operations. This preliminary action prepares the data structure in advance, reducing the complexity of real-time fusion operations and enabling more efficient processing during actual tracking.
3Area of stationary object
If multiple cameras are used for obstacle tracking, then tracking coverage is improved, but identifier matching accuracy deteriorates due to lack of unified identification
Solution Approach 1:
The system implements a universal global identifier system that serves multiple cameras simultaneously. Each camera maintains its local identifiers while the global identifier provides a unified identification mechanism across all cameras, enabling accurate matching of obstacles across different camera views and expanding tracking coverage without sacrificing identification accuracy.
Data Source
AI summary
Embodiments of the present provide a cross-camera obstacle tracking method, system and medium. The method includes: obtaining obstacle tracking results of images captured by at least two cameras, wherein each obstacle tracking result comprises results after identifying and tracking at least one obstacle in the image captured by each camera, and each obstacle is labeled with a local identifier for each camera; in response to a triggering condition of a main camera in the cameras to fusion of cross-camera obstacles being satisfied, establishing a mapping relation between local identifiers of the obstacles in the cameras and global identifiers according to the obstacle tracking result of each of the cameras; determining similarities of obstacles according to the obstacle tracking result of each of the cameras, and performing fusion of the global identifiers on the same obstacle according to the similarities; and obtaining final obstacle tracking results labeled with the global identifiers.