Security Camera System for Lost Item Tracking via Hypothesis Graph
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Solution Overview
Problem
People commonly lose or misplace items and spend significant time trying to locate them, with existing security monitoring systems not effectively addressing this issue without requiring additional sensors or providing accurate tracking.
Innovation Solution
A security monitoring system that uses data from existing sensors, including cameras, to locate and track items by building a hypothesis graph based on live and historical video data, allowing occupants to communicate the item's description and last seen location, and providing potential locations with likelihoods, leveraging machine learning for improved accuracy over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If existing security monitoring systems are used without additional sensors, then device complexity is reduced, but measurement precision and tracking accuracy deteriorate
Solution Approach 1:
The patent makes existing security monitoring sensors (cameras, motion detectors) perform multiple functions: their primary security surveillance function continues while simultaneously enabling item tracking and location capabilities. The system processes video feeds and sensor data to detect both security events and item positions, allowing one system to serve dual purposes without adding dedicated tracking sensors.
2Device complexity
If manual searching methods are used to locate items, then no additional tracking infrastructure is needed, but loss of time increases significantly
Solution Approach 1:
The system performs preliminary tracking and analysis of item locations continuously in the background using existing sensor data. When an item is lost, the hypothesis graph and probability calculations are already prepared or can be quickly generated, eliminating the need for manual searching. The system proactively monitors and records item positions rather than reacting only when items are reported missing.
3Measurement precision
If hypothesis graph analysis with multiple potential locations is performed, then measurement precision improves, but device complexity and computational requirements increase
Solution Approach 1:
The system calculates probabilities for multiple potential item locations using hypothesis graphs, but presents only the most likely locations to users rather than all possible locations. This partial action approach provides sufficiently accurate results for practical purposes while reducing the computational burden of analyzing and presenting every conceivable location scenario.
Solution Approach 2:
The hypothesis graph acts as an intermediary computational model that organizes and structures the complex relationships between sensor data, item characteristics, and potential locations. Rather than directly computing all possible location scenarios, the system uses the hypothesis graph framework to mediate the analysis, making the computational process more manageable and systematic.
4Measurement precision
If additional sensors are deployed to improve tracking accuracy, then measurement precision improves, but device complexity and cost increase
Solution Approach 1:
The patent makes existing security monitoring sensors (cameras, motion detectors) perform multiple functions: their primary security surveillance function continues while simultaneously enabling item tracking and location capabilities. The system processes video feeds and sensor data to detect both security events and item positions, allowing one system to serve dual purposes without adding dedicated tracking sensors.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for finding lost objects. In some implementations, a request for a location of an item is obtained. Current video data from one or more cameras is obtained. It is determined that the item is not shown in the current video data. Sensor data corresponding to historical video data is obtained. Events that likely occurred with the item and corresponding likelihoods for each of the events are determined. A likely location for the item is determined based on the likelihoods determined for the events. An indication of the likely location of the item is provided.