Multi-Camera Tracking via Trajectory Clustering and Cost-Minimum Path
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Solution Overview
Problem
Current multi-camera multi-target tracking technologies face challenges in accuracy due to factors like occlusion, illumination, and attitude changes, leading to incorrect cross-camera target trajectories and identification-switch issues.
Innovation Solution
A method involving single-camera multi-target tracking and multi-camera matching, which includes clustering local target trajectories, implementing a cost-minimum path algorithm on a directed graph, and merging corresponding trajectories to improve accuracy and reduce identification-switch.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If multiple cameras are used to perform monitoring and target tracking for a larger space, then the monitoring coverage is improved, but the accuracy of tracking results deteriorates due to occlusion, illumination, and attitude changes
Solution Approach 1:
The patent divides the tracking process into two stages: single-camera target tracking to obtain local trajectories, and inter-multi-camera matching to associate trajectories across cameras. This segmentation allows each camera to independently track targets within its own monitoring area with high accuracy, while the matching stage handles the complexity of cross-camera association separately, thus maintaining tracking accuracy across multiple cameras.
Solution Approach 2:
The patent introduces tracklets as intermediary objects that bridge single-camera tracking results and multi-camera matching. Tracklets contain trajectory information from individual cameras and serve as the basis for inter-camera association. This intermediary structure decouples the complexity of multi-camera tracking into manageable components, allowing accurate trajectory association despite occlusion, illumination changes, and attitude variations.
2Adaptability or versatility
If traditional multi-camera multi-target tracking technology is used, then the system can handle multiple targets across cameras, but incorrect cross-camera target trajectories and identification-switch issues occur
Solution Approach 1:
The patent implements a feedback mechanism in the inter-multi-camera matching stage where trajectory associations are continuously refined. The system uses cost functions to evaluate matching quality and iteratively improves trajectory associations by feedback from matching results. This feedback loop corrects potential identification-switch errors and ensures trajectory correctness across multiple cameras while maintaining multi-target handling capability.
Data Source
AI summary
The present disclosure relates to a method, an apparatus and a storage medium for multi-target multi-camera tracking. According to an embodiment of the present disclosure, the method comprises: determining an overall local target trajectory set including a local target trajectory set of each camera by performing single-camera multi-target tracking on a corresponding image sequence provided by each camera of a plurality of cameras; and determining a global target trajectory set for the plurality of cameras by performing multi-camera multi-target matching on the overall local target trajectory set; wherein determining the global target trajectory set comprises: determining a cluster matched global trajectory set by clustering local target trajectories; determining a cost-minimum path set by implementing a cost-minimum path algorithm on a directed graph; and merging corresponding trajectories in the cluster matched global trajectory set based on the cost-minimum path set.