Collaborative Target Tracking Calibration via Prediction Dictionary
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Solution Overview
Problem
Multi-target monitoring systems face challenges in efficiently detecting and characterizing targets in dynamic environments due to complex algorithms, hardware requirements, and the need for accurate correlation metrics, often resulting in erroneous detection information and difficulties in adapting to new or changing target environments.
Innovation Solution
A calibration process for mono or multi-target collaborative monitoring systems that creates a prediction dictionary and 'Territory' list to associate monitoring algorithms with confidence scores, updating a 'chess zone' to improve target detection by identifying reliable algorithms and reducing false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple tracking algorithms are used to improve detection accuracy, then the reliability of target detection is improved, but the device complexity increases
Solution Approach 1:
The system segments the tracking functionality by assigning different tracking algorithms to different tracking devices, each responsible for specific targets or regions. This allows the complex multi-algorithm system to be managed through modular decomposition, where each algorithm operates independently on designated tasks while contributing to overall system reliability.
Solution Approach 2:
The patent merges multiple tracking algorithms and their outputs into a unified collaborative tracking system. By combining the results from multiple algorithms through correlation metric evaluation and consensus mechanisms, the system achieves higher detection reliability while managing complexity through integrated processing.
2Productivity
If multiple hardware components are used to execute tracking algorithms, then the processing capability is improved, but the device complexity increases
Solution Approach 1:
The patent implements multi-functionality by enabling standard processors to execute specialized tracking algorithms that would traditionally require dedicated hardware. Through software-based algorithm implementation, the system achieves versatile processing capability across different tracking tasks using uniform hardware platforms, thereby improving productivity without proportionally increasing hardware complexity.
3Reliability
If correlation metric threshold is adjusted to reduce false positives, then the reliability of detection is improved, but the productivity of target detection decreases
Solution Approach 1:
The system dynamically adjusts correlation metric thresholds based on the specific tracking task, target characteristics, and operational context. Rather than using fixed thresholds, the system adapts threshold values in real-time to optimize the balance between reducing false positives and maintaining high detection rates, thereby simultaneously improving reliability and productivity.
Solution Approach 2:
The patent employs parameter changes by modifying correlation metric thresholds and other detection parameters based on feedback from tracking performance evaluation. The system learns from previous detections and adjusts parameters to achieve optimal performance, resolving the contradiction between detection accuracy and detection rate through adaptive parameter tuning.
Data Source
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AI summary
The present invention relates to the calibration of a single or multi-target collaborative tracking system comprising at least one set of tracking algorithms T executed by at least one processor to process information from an image stream for target detection in the video image stream captured by at least one camera of the tracking system. The single or multi-target tracking system comprises at least one set of modules/programs stored in memory and at least one processor executing all the modules to implement the calibration method, which includes collecting the associations of all the tracking algorithms T of the single or multi-target tracking system to create a prediction dictionary Dictpredictions for a given target, and determining it via the created prediction dictionary Dictpredictions.The T-tracking algorithms in reference areas to create a "ground truth" list. This list includes T-tracking algorithms with a confidence score above a predefined threshold value, and updates the known failure areas of each T-tracking algorithm from the list of T-tracking algorithms of the single or multi-target tracking system that are not part of the "ground truth" list and have a likelihood score below a predetermined threshold.