Mobile Device Inference for Moving Object Tracking
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Solution Overview
Problem
Current tracking systems, such as surveillance systems, face challenges in quickly identifying and predicting the location of objects of interest, like fugitives or missing persons, due to static data and limited real-time tracking capabilities, which can lead to delayed pursuit and increased community risk.
Innovation Solution
A computer-implemented method and system that infers the mobile device of an object of interest by associating it with a transceiver base station, obtaining a list of active mobile devices within its range, and generating location prediction estimates using cellular network data and security camera inputs, enabling efficient tracking and prediction of the object's movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static surveillance data is used for tracking, then system simplicity is maintained, but tracking speed and real-time capability deteriorate
Solution Approach 1:
The patent combines multiple data sources (surveillance camera data, cellular network data, GPS data) and processing systems into an integrated tracking platform. This merging enables real-time object identification and location prediction by synthesizing information from diverse sources, thereby improving tracking speed without requiring a complete system overhaul.
Solution Approach 2:
The tracking system is designed to handle multiple types of data inputs (visual surveillance, cellular signals, GPS) and perform multiple functions (object identification, location tracking, prediction). This multi-functional approach allows the system to process various data types through unified algorithms, enhancing productivity while maintaining manageable complexity through standardized processing pipelines.
2Measurement precision
If traditional surveillance systems are used, then implementation simplicity is maintained, but real-time location prediction capability deteriorates
Solution Approach 1:
The patent introduces cellular network infrastructure (base stations, mobile device signals) as an intermediary layer between traditional surveillance cameras and the tracking system. This intermediary provides continuous location data through mobile device associations with base stations, enabling precise real-time location prediction without requiring direct line-of-sight surveillance coverage.
Solution Approach 2:
The system replaces purely mechanical/optical surveillance methods with electronic and computational approaches. Instead of relying solely on physical camera coverage and manual analysis, the patent uses automated algorithms to process cellular network data, GPS signals, and surveillance footage, substituting mechanical surveillance with electronic data synthesis and computational prediction.
3Productivity
If extended search time is allowed, then identification accuracy may improve, but community risk and response time deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-establishing associations between objects of interest and their mobile devices before pursuit begins. When an object is identified by surveillance, the system has already linked it to cellular network identifiers and location patterns, enabling immediate tracking and prediction without delay for initial identification and association.
Solution Approach 2:
The tracking system implements continuous feedback loops where location predictions are constantly updated based on new surveillance data, cellular network information, and movement patterns. This real-time feedback refines identification reliability during the pursuit, allowing the system to maintain high confidence in tracking accuracy while responding rapidly to changing conditions.
Data Source
AI summary
A first set of data may be received indicating that an object of interest has been identified. A second set of data may be received indicating a first location of where the object of interest was identified. The first location may correspond to a geographical area. In response to the receiving of the first set of data and the second set of data, the first location may be associated with a first transceiver base station. In response to the associating, a first list of one or more mobile devices may be obtained that are within an active range of the first transceiver base station.