3D Object Tracking With Physics Validation Against False Positives
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Solution Overview
Problem
Existing systems for tracking objects in motion, such as a golf ball in flight, struggle with high false positive rates from sensor data, leading to overload and inability to accurately detect objects in real-time due to the inclusion of many inaccurate detections and data pairings, resulting in missed detections (false negatives).
Innovation Solution
A method and system that utilizes unverified object detections from multiple sensors, allowing more false positives to minimize false negatives by using a filter to form hypotheses of objects in motion, applying a physics model to verify and track objects in 3D space, and employing models of varying complexity based on detection thresholds to refine hypotheses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If unverified detections from multiple sensors are used to minimize false negatives, then detection coverage is improved, but false positive rate increases causing system overload
Solution Approach 1:
The system performs preliminary filtering of sensor detections before full 3D tracking processing. Detections are pre-processed to identify potential objects of interest, forming hypotheses that are then validated. This preliminary action reduces the burden on the main tracking system by eliminating obvious false positives early in the pipeline.
Solution Approach 2:
The patent introduces an intermediary filtering layer between raw sensor detections and final tracking output. This intermediary system uses multiple criteria (temporal consistency, spatial plausibility, detection confidence) to mediate between the high-volume unverified detections and the tracking algorithm, preventing system overload while maintaining detection coverage.
2Measurement precision
If strict verification is applied to sensor detections, then false positives are reduced, but false negatives increase causing missed detections
Solution Approach 1:
The verification threshold is made dynamic rather than static. The system adjusts the stringency of verification based on contextual factors such as object probability, detection history, and current tracking state. This allows the system to be more lenient when detection coverage is critical and more strict when precision is paramount, resolving the contradiction between false positives and false negatives.
Solution Approach 2:
The system changes verification parameters adaptively based on the detection context. Instead of applying a fixed verification threshold, the system modulates verification strictness according to factors like object class probability, detection confidence scores, and temporal patterns, allowing optimal balance between precision and coverage in different situations.
3Reliability
If multiple sensors are combined to improve detection reliability, then detection coverage is enhanced, but data processing complexity increases
Solution Approach 1:
The data processing pipeline is segmented into distinct stages: initial detection filtering, hypothesis formation, validation, and final tracking. Each sensor type and detection stage is processed separately through specialized sub-routines, allowing the system to handle multiple sensors without monolithic complexity. This segmentation enables modular processing of multi-sensor data.
Solution Approach 2:
The system transitions from 2D sensor data to 3D spatial hypotheses, using the additional dimensional information to resolve ambiguities. By elevating detections to 3D space and applying spatial consistency checks, the system efficiently filters false positives from multiple sensors without requiring exhaustive processing of all sensor combinations.
4Speed
If real-time tracking is implemented, then response speed is improved, but processing accuracy decreases due to computational constraints
Solution Approach 1:
The system implements periodic verification and validation cycles at strategically spaced intervals rather than continuous processing. Between verification points, tracking proceeds with reduced checks to maintain speed. This periodic approach allows real-time response while periodically ensuring accuracy, balancing computational constraints with tracking precision.
Data Source
AI summary
Methods, systems, and apparatus, including medium-encoded computer program products, for three-dimensional object tracking includes, in at least one aspect, a method including: obtaining three-dimensional positions of objects registered by a detection system configured to allow more false positives so as to minimize false negatives, forming hypotheses using a filter that allows connections between registered objects when estimated three-dimensional velocity vectors roughly correspond to an object in motion in three-dimensional space, eliminating a proper subset of the hypotheses that are not further extended during the forming, specifying at least one three-dimensional track of at least one ball in motion in three-dimensional space by applying a full three-dimensional physics model to data for the three-dimensional positions used in the forming of at least one hypothesis that survives the eliminating, and outputting for display the at least one three-dimensional track of the at least one ball in motion in three-dimensional space.