Batch Detection Segmentation for Dense Target Tracking
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Solution Overview
Problem
Existing real-time tracking systems, such as multiple hypothesis trackers, face significant computational challenges when dealing with high detection density environments, leading to poor performance and increased computational requirements, especially when objects have multiple scatterers.
Innovation Solution
The system processes batch detection information in multiple stages of varying complexity, initially performing low-complexity processing to approximate object motion and then redistributing detections for subsequent higher complexity processing, allowing for efficient parallelization and improved target tracking in dense environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a long batch period is used to increase detection information, then tracking precision is improved, but computational requirements increase dramatically
Solution Approach 1:
The patent segments the batch detection processing into multiple stages: a first stage that processes all detections with low complexity to create initial associations, and a second stage that processes only selected detections with higher complexity to refine associations. This segmentation allows the system to achieve high tracking precision through extended batch periods while controlling computational requirements by not processing all detections at maximum complexity.
Solution Approach 2:
The patent applies different processing complexities to different subsets of detections. The first stage uses low complexity processing for all detections, while the second stage applies higher complexity processing only to selected detections that require refinement. This local quality approach enables the system to maintain high precision for critical tracking tasks while reducing overall computational burden.
2Productivity
If a short batch period is used to reduce computational requirements, then processing efficiency is improved, but tracking performance deteriorates in dense environments
Solution Approach 1:
The patent divides batch detection processing into two stages that operate on different subsets of detections. The first stage processes all detections with low complexity to establish initial associations quickly, while the second stage refines only the most critical associations. This segmentation enables the system to maintain processing efficiency with short batch periods while achieving reliable tracking performance through targeted refinement in the second stage.
Solution Approach 2:
The patent applies partial action by processing only a subset of detections in the second stage with higher complexity, rather than processing all detections at maximum complexity. This partial processing approach maintains processing efficiency while providing sufficient refinement for reliable tracking in dense environments.
3Measurement precision
If multiple scatterers are detected, then object identification accuracy is improved, but computational requirements increase significantly
Solution Approach 1:
The patent segments the processing of multiple scatterers into two stages. The first stage identifies and associates detections with scatterers using low complexity processing, creating initial groupings. The second stage then processes only these pre-grouped scatterer associations with higher complexity to refine identification accuracy. This segmentation enables the system to handle multiple scatterers accurately while controlling computational requirements by avoiding the need to process all possible scatterer combinations at maximum complexity.
Data Source
AI summary
The embodiments described herein relate to systems and techniques for processing batch detection information received from one or more sensors configured to observe objects of interest. In particular the systems and techniques are configured to enhance track performance particularly in dense target environments. A substantially large number of batch detections can be processed in a number of phases of varying complexity. An initial phase performs relatively low complexity processing on substantially all detections obtained over an extended batch period, approximating object motion with a simplified model (e.g., linear). The batch detections are divided and redistributed into swaths according to the resulting approximations. A subsequent phase performs greater complexity (e.g., quadratic) processing on the divided sets of detections. The subdivision and redistribution of detections lends itself to parallelization. Beneficially, detections over extended batch periods can be processed very efficiently to provide improved target tracking and discrimination in dense target environments.