Object Tracking Filters Reduce Matrix Processing Time
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Solution Overview
Problem
Current object tracking systems face challenges in processing information in a timely manner due to the time-consuming processing of matrices associating detected objects, which can be costly to overcome with robust hardware.
Innovation Solution
An object tracking system that uses localized groups to reduce the size of matrices processed by optimization filters, predicting the location of dynamic objects, and employing filters to associate objects based on distance and probability, thereby minimizing processing time without the need for robust hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If optimization filters process matrices associating all detected objects, then association accuracy is improved, but processing time increases significantly
Solution Approach 1:
The patent segments the object tracking problem into two distinct phases: a coarse association phase using a first filter with simplified criteria (distance, velocity, acceleration thresholds) to quickly match most objects, and a fine association phase using a second optimization filter only on remaining unassociated objects. This segmentation reduces the computational burden on the optimization filter while maintaining overall association accuracy.
Solution Approach 2:
The first filter acts as an intermediary between object detection and the optimization filter. It performs preliminary association using efficient threshold-based comparisons, filtering out the majority of objects before they reach the computationally intensive optimization filter. This intermediary step significantly reduces processing time while preserving association accuracy for the remaining objects.
2Productivity
If robust hardware with high processing speed and memory is implemented, then processing capability is improved, but system cost increases
Solution Approach 1:
The patent changes the processing parameters by implementing a two-stage filtering approach with different computational complexities. The first filter uses simple threshold comparisons with low computational requirements, while the second optimization filter handles only the remaining objects. This parameter change in processing strategy achieves high productivity without requiring expensive robust hardware.
3Reliability
If all objects are processed through the optimization filter, then association completeness is improved, but processing efficiency decreases
Solution Approach 1:
The patent segments the object population into associated and unassociated groups after the first filtering stage. The optimization filter is applied only to the unassociated group, ensuring completeness for those objects while maintaining high processing efficiency by excluding already-associated objects from further computation.
Solution Approach 2:
Instead of applying the optimization filter to all objects (excessive action), the patent applies it only to the subset of unassociated objects (partial action). This partial application maintains association completeness for the necessary objects while significantly improving processing efficiency by avoiding redundant computations on already-associated objects.
Data Source
AI summary
An object tracking system and method operable to minimize processing time for tracking objects is provided. The system includes a pair of filters operable to make associations between dynamic objects newly detected and previously detected. One of the pair of filters makes an association when the predicted location of the previously detected objects is within a predetermined distance of the newly detected object. The other of the pair of filters makes an association based upon the probability that a newly detected dynamic object is a previously detected dynamic object. The remaining unassociated dynamic objects are then localized so as to form discrete matrices for optimization filters.