Cloud Camera Event Detection via Intermediary Server
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Solution Overview
Problem
Existing network-connected camera systems face challenges in scalability, firewall traversal, and user configurability, making it difficult to monitor large numbers of cameras and users while ensuring security and real-time event notification.
Innovation Solution
A system utilizing scalable cloud computing with automated service provisioning, virtual machine migration, and RESTful APIs, combined with 'heartbeat' status messages and encryption, allows for widespread use of millions of cameras and users, enabling real-time event detection and notification through mobile devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional camera systems are used for monitoring, then basic video capture is achieved, but scalability to millions of cameras and users cannot be achieved
Solution Approach 1:
The patent introduces cloud servers as intermediaries between cameras and user devices. The servers receive video streams from multiple cameras, perform centralized processing including motion detection and event recognition, then deliver relevant information to users. This intermediary architecture allows the system to scale to millions of cameras without requiring direct peer-to-peer connections, thereby managing system complexity while enabling massive scalability.
2Speed
If cameras are directly connected to user devices, then real-time monitoring is achieved, but firewall traversal and network connectivity issues arise
Solution Approach 1:
Cloud servers act as mediators that receive video streams from cameras and push notifications to user devices. This eliminates the need for direct camera-to-device connections, bypassing firewall and NAT traversal issues. The servers maintain persistent connections with both cameras and users, enabling real-time communication through a centralized hub that handles network complexity.
Solution Approach 2:
The system implements automated service provisioning where new cameras and users are automatically registered and integrated into the system without manual configuration. The cloud servers automatically manage connection establishment, authentication, and data routing, eliminating the need for users to manually configure network settings or traverse firewalls.
3Reliability
If manual security monitoring is implemented, then event detection accuracy is achieved, but user time and effort requirements increase
Solution Approach 1:
The system implements automated event detection through server-side processing of video streams. Motion detection algorithms and event recognition systems continuously analyze incoming video data, automatically identifying and categorizing events without human intervention. This self-service approach maintains high detection accuracy while completely eliminating the need for users to manually monitor camera feeds.
Solution Approach 2:
The system provides automated feedback to users through push notifications and alerts when events are detected. The server analyzes video streams in real-time, identifies significant events based on predefined criteria, and immediately notifies relevant users. This closed-loop feedback system ensures reliable event detection while freeing users from continuous monitoring, as they receive targeted alerts only when actual events occur.
4Loss of information
If comprehensive video storage is implemented, then event review capability is achieved, but storage costs and data management complexity increase
Solution Approach 1:
The system extracts and stores only the most relevant information from continuous video streams. Instead of archiving all video data, the server detects events, extracts short video clips surrounding detected events, and stores only these extracted segments. This selective extraction approach preserves all necessary event information for review while dramatically reducing storage requirements compared to comprehensive video archiving.
Data Source
AI summary
The invention is based, in part, on a system for allowing at least one client to real-time monitor and, or playback at least one real-world recognized event via at least one processor-controlled video camera, said system comprising: a processor; a non-transitory storage medium coupled to the processor; encoded instructions stored in the non-transitory storage medium, which when executed by the processor, cause the processor to: detect a threshold-grade event from audio-video data of a real-world environment captured from a processor-controlled video camera by an event detection module within an event management system applying event detection parameters; analyze the threshold-grade event for categorization into any one of a recognized event by an event recognition module within the event management system applying event recognition parameters; transmit at least any one of a single stream of the recognized event and, or a single stream of a audio-video sequence succeeding and, or preceding the recognized event to a client device; and facilitate at least any one of a user defined playback of the single stream of the recognized event, user defined monitoring of the audio-video sequence preceding and, or succeeding the recognized event, and, or remote provisioning of the processor-controlled video camera, whereby the playback and, or provisioning is facilitated via a client device user interface.