Cross-Agency Video Analytics for Context-Aware Event Reclassification
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Solution Overview
Problem
Existing video analytics systems in public-safety agencies often inaccurately classify events due to limited data access, leading to unintended consequences such as misidentification and strained community relations.
Innovation Solution
Implement a collaboration method between public-safety and non-public-safety agencies using separate video analytics engines trained on different datasets to verify event classifications, allowing for reclassification of events based on data from both agencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video analytics systems are trained using limited data from a single agency, then the system can operate independently, but the event classification accuracy deteriorates
Solution Approach 1:
The system is divided into multiple independent video analytics engines, each trained on data from a specific agency (public-safety and non-public-safety). Each engine operates independently to classify events, and their results are then integrated through a collaboration module. This segmentation allows each engine to maintain high accuracy with agency-specific data while the overall system benefits from combined insights.
Solution Approach 2:
The patent merges the outputs of multiple independent video analytics engines by implementing a collaboration mechanism where engines share classification results. The system combines classifications from public-safety agency engines and non-public-safety agency engines to produce a final, more accurate event classification, thereby improving overall measurement precision through data integration.
2Measurement precision
If multiple video analytics engines from different agencies are integrated, then event classification accuracy improves, but the system complexity increases
Solution Approach 1:
The collaboration module is designed with universal functionality to handle classifications from multiple different video analytics engines. It implements a standardized interface that can process and integrate results from various agency-specific engines without requiring custom integration logic for each agency, thereby managing complexity while maintaining improved classification accuracy.
Solution Approach 2:
The patent introduces a collaboration module as an intermediary between multiple video analytics engines. This mediator receives classifications from different agencies, reconciles potential conflicts, and produces a final integrated classification. The intermediary simplifies the interaction complexity by providing a standardized coordination layer between independent engines.
3Device complexity
If a single agency operates its video analytics system independently, then the system is simpler to manage, but false alarms increase due to limited data context
Solution Approach 1:
The collaboration system implements feedback mechanisms where video analytics engines share their classification results and learn from each other's determinations. When one engine flags an event as abnormal, it can query other engines for their assessments, and the system uses this feedback to adjust classifications and reduce false alarms caused by limited single-agency data context.
Solution Approach 2:
The system performs preliminary classification actions using agency-specific engines, then uses the collaboration module to verify and adjust these preliminary results before finalizing event classification. This preliminary action approach allows each engine to operate with relative simplicity while the collaboration layer ensures reduced false alarms through additional data context.
Data Source
AI summary
A process of collaboration between different agencies for classifying an event captured in a video stream. In operation, a video stream captured by a camera operated by a public-safety agency is analyzed using a first video analytics engine trained using a first set of video analytics data associated with the public-safety agency and an abnormal event with respect to a person or object captured in the video stream is detected. When it is determined that the video stream is captured at a location that is proximity to an operating environment of a non-public-safety agency, a query is transmitted to a second video analytics engine trained using a second set of video analytics data associated with the non-public-safety agency. The abnormal event is reclassified as a normal event when a response from the second video analytics engine indicates that the abnormal event is normal within the operating environment of the non-public-safety agency.