Multi-Object Tracking Trajectory Optimization for Outlier Correction
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Solution Overview
Problem
Existing multi-object tracking technologies face challenges in accurately and efficiently processing sensor data to obtain robust motion trajectories due to issues like data association, outlier detection, and computational complexity, leading to inaccurate and inefficient tracking results.
Innovation Solution
A motion trajectory optimization apparatus and method that includes an acquisition module, tracking module, and optimization module to process sensor data, detect outliers, and optimize motion trajectories by correcting global and local outliers, inconsistent geometric parameters, and ID-switching segments using self-supervised learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If online data processing uses a high filtering threshold to eliminate noise data, then processing efficiency is improved, but useful information is lost leading to inaccurate motion trajectories
Solution Approach 1:
The system performs preliminary action by pre-calculating and storing motion models and trajectory predictions before actual data processing. This allows the online filtering stage to work with pre-prepared reference data, enabling efficient noise filtering without losing useful information, as the filtering can compare against predetermined motion patterns rather than requiring complex real-time analysis.
Solution Approach 2:
The system applies dynamics by adaptively adjusting filtering thresholds based on the specific motion characteristics of each target object. Instead of using a fixed high threshold, the system dynamically modifies filtering parameters according to detected motion patterns, allowing efficient processing while preserving useful information that matches actual motion behavior.
2Measurement precision
If offline data processing processes all sensor data without filtering thresholds, then measurement completeness is improved, but computational complexity increases leading to inefficient processing
Solution Approach 1:
The system segments the data processing task into distinct stages: offline pre-processing where motion models are learned and stored, and online processing where actual filtering occurs using these pre-computed models. This segmentation allows comprehensive data analysis to be performed offline without burdening the real-time system, reducing online computational complexity while maintaining data completeness.
Solution Approach 2:
The system performs preliminary action by conducting extensive data analysis and motion model learning during offline processing before deployment. This pre-computation of motion patterns and trajectory expectations allows the online system to work with simplified data and reduced computational requirements, achieving efficient processing without sacrificing measurement completeness.
3Measurement precision
If data association is performed to correlate detected target objects, then motion trajectory accuracy is improved, but processing time increases
Solution Approach 1:
The system applies feedback by using predicted trajectory information from motion models to guide the data association process. Instead of performing exhaustive correlation analysis, the system uses feedback from previous tracking states and motion predictions to pre-filter and prioritize candidate associations, significantly reducing processing time while maintaining trajectory accuracy through informed data correlation.
Solution Approach 2:
The system performs preliminary action by pre-computing motion models and trajectory predictions that encode expected association patterns. This preliminary analysis of target object behavior allows the data association stage to quickly identify correct matches without exhaustive computation, reducing processing time while preserving trajectory accuracy through pre-analyzed motion characteristics.
Data Source
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AI summary
The present invention provides a multi-object tracking system and its motion trajectory optimization apparatus and optimization method. The motion trajectory optimization apparatus comprises: an acquisition module configured to acquire target object data from sensors perceiving multiple target objects; a tracking module configured to track the target object data to obtain multiple motion trajectories of the multiple target objects, each motion trajectory including multiple detection results of a target object at multiple moments; and an optimization module configured to perform at least one of the following optimizations on the multiple motion trajectories: - optimizing motion trajectories containing potential global outlier detection results; -optimizing motion trajectories containing potential local outlier detection results; -optimizing motion trajectories containing inconsistent geometric parameters; and -optimizing motion trajectories containing potential ID-switching trajectory segments.