Multi-Camera Event Synchronization via Cloud Correlation
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Solution Overview
Problem
Current video surveillance systems lack the ability to effectively synchronize and correlate events across multiple cameras, leading to inefficiencies in monitoring and summarization of video data.
Innovation Solution
A computer-implemented method for comparing events across multiple videos by determining feature descriptions, locations, and timestamps, and classifying them as correlated or not, using a system with video data storage and a video processor that can execute algorithms to identify and track events across multiple cameras.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple cameras are used for video surveillance, then monitoring coverage and reliability are improved, but event correlation and synchronization across cameras become more difficult
Solution Approach 1:
The patent introduces a cloud platform as an intermediary that receives video streams from multiple cameras, performs centralized event detection and correlation, and synchronizes events across camera views. This mediator handles the complex task of multi-camera coordination, relieving individual cameras and local systems of this burden.
Solution Approach 2:
The system implements feedback mechanisms where detected events from one camera trigger searches for corresponding events in other camera feeds. The correlation results feed back into the monitoring system to confirm or refine event identification across multiple views, improving reliability through cross-validation.
2Measurement precision
If manual monitoring of multiple camera feeds is performed, then detailed event analysis is possible, but time consumption and operational efficiency decrease
Solution Approach 1:
The system employs automated event detection algorithms that independently analyze video feeds, identify events of interest, and correlate them across cameras without human intervention. The automated system serves itself by performing detection, correlation, and initial analysis tasks that would otherwise require manual monitoring.
Solution Approach 2:
The patent replaces manual mechanical monitoring with automated computational systems. Machine learning algorithms and computer vision techniques substitute human operators for detecting and correlating events, maintaining analytical precision while dramatically improving processing speed and efficiency.
3Measurement precision
If video data from multiple cameras is stored and analyzed, then event correlation accuracy is improved, but data storage requirements and processing complexity increase
Solution Approach 1:
The system extracts only relevant event information and metadata from full video streams for storage and correlation purposes. Instead of storing and processing entire video datasets from multiple cameras, the system extracts key event features, timestamps, and location data, significantly reducing data volume while maintaining correlation accuracy.
Solution Approach 2:
The patent segments video analysis into distinct processing stages: initial event detection in individual feeds, extraction of event features, correlation of extracted events across cameras, and detailed analysis only of correlated events. This segmentation allows efficient handling of large video datasets by processing only relevant portions at each stage.
Data Source
AI summary
A computer-implemented method to compare events from videos including monitoring two or more videos and identifying a first event in a first video, determining a first feature description of the first event, a first location of the first event, and a first time stamp of the first event, identifying a second event in a second video of the two or more videos, determining a second feature description of the second event, a second location of the second event, and a second time stamp of the second event, comparing the first and second feature description, the first and second location, and the first and second time stamp. The method may include classifying the first event and the second event as correlated events determined to be sufficiently similar and classifying the first event and the second event as not correlated events when determined not to be sufficiently similar.