Mobile Body Track Identification System Using Likelihood Hypothesis
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Solution Overview
Problem
Current mobile body tracking systems struggle to accurately identify which mobile body matches a specific track, especially when multiple mobile bodies are at the same position, leading to frequent interruptions and reduced tracking precision due to limited detection capabilities of cameras and sensors.
Innovation Solution
A mobile body track identification system that generates track-coupling candidates and identification pairs, using a likelihood calculation method to estimate the most probable hypothesis based on detection probabilities and environmental information, ensuring accurate tracking even with interruptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cameras and sensors are used to track mobile bodies, then tracking capability is provided, but identification accuracy deteriorates when multiple mobile bodies are at the same position
Solution Approach 1:
The system performs preliminary actions by detecting mobile bodies and creating tracks before identification occurs. Multiple detection devices capture positional information and create tracks in advance, allowing the system to prepare tracking data before the identification step, thereby resolving the contradiction between having tracking capability and achieving accurate identification when mobile bodies overlap.
Solution Approach 2:
The patent introduces an intermediary approach by using detection devices and track management as intermediate steps between mobile body presence and final identification. The system uses detected positional information and created tracks as intermediaries to bridge the gap between mere detection and accurate identification, enabling the system to handle cases where multiple mobile bodies are at the same position.
2Duration of action of moving object
If tracking resumes based on newly extracted information after interruption, then tracking continuity is maintained, but tracking precision deteriorates
Solution Approach 1:
The system performs preliminary actions by creating and maintaining tracks continuously even when identification is interrupted. By preparing track data in advance and maintaining it in memory, the system can resume tracking without losing precision, as the track already contains historical positional information that guides the resumption point accurately.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors detection results and compares them with existing tracks. When interruption occurs, the feedback loop enables the system to identify the correct track to resume based on previously established patterns and positional data, thereby maintaining both continuity and precision.
3Measurement precision
If multiple detection devices are deployed to improve identification, then identification accuracy improves, but device complexity increases
Solution Approach 1:
The patent applies universality by designing detection devices that serve multiple functions: they detect mobile bodies, create tracks, provide positional information, and support both identification and tracking operations. This multi-functionality reduces the need for separate specialized devices, thereby improving identification accuracy while controlling overall system complexity.
Solution Approach 2:
The system merges the functions of detection, tracking, and identification into an integrated approach. By combining multiple detection devices and their outputs into a unified track management system, the patent achieves improved identification accuracy while managing complexity through functional integration rather than separate independent systems.
Data Source
AI summary
There is provided a mobile body track identification system that determines which mobile body matches which detected track with a high precision irrespective of frequent interruption of tracks of a mobile body detected in a tracking area. Herein, hypotheses are generated by use of sets of track-coupling candidate/identification pairs, which combines track-coupling candidates, combining tracks of a mobile body detected in a predetermined time in the past, and identifications of the mobile body and which satisfies a predetermined condition. Next, identification likelihoods are calculated as likelihoods of detecting identifications in connection with tracks indicated by track-coupling candidates included in track-coupling candidate/identification pairs ascribed to each of the selected hypotheses. Identification likelihoods are integrated per each track-coupling candidate/identification pair, thus calculating an identification likelihood regarding the selected hypothesis. A most-probable hypothesis is estimated based on identification likelihoods of hypotheses.