Edge Video Surveillance With Quarantined Cameras and Event Uploads
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Solution Overview
Problem
Existing video surveillance systems are expensive, require remote servers for processing, incur high computational costs, and pose security risks due to unregulated cameras that can be hacked, and they generate excessive data storage and transmission bandwidth.
Innovation Solution
A networked video surveillance platform with local video recorders and edge computer vision algorithms that perform object detection and recording, using a quarantined sub-network to prevent unauthorized access, and multi-stage hierarchical computer vision models for efficient data processing and storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If remote servers are used to process captured images, then object detection and classification can be performed, but computational costs and data transmission bandwidth requirements increase significantly
Solution Approach 1:
The system segments video data into discrete clips based on detected events (e.g., motion detection, object identification). Only clips containing significant events are processed and transmitted to remote servers, while normal footage is processed locally or discarded. This segmentation reduces the volume of data requiring expensive remote computational resources while maintaining detection accuracy for important events.
Solution Approach 2:
The system implements local video recording and edge-based computer vision processing at distributed recording devices rather than centralized remote servers. Each recording device performs local analysis to determine which video clips warrant transmission, reducing the computational burden on remote servers while maintaining detection capabilities through a distributed architecture.
2Reliability
If all video data is transmitted to remote servers, then comprehensive surveillance coverage is achieved, but data transmission bandwidth and storage requirements become excessive
Solution Approach 1:
The system performs preliminary processing of video data locally at recording devices by detecting events (motion, objects, anomalies) before transmission. Video clips are pre-screened and only those containing detected events are transmitted to remote servers, eliminating the need to transmit entire video feeds while maintaining comprehensive surveillance of significant events.
Solution Approach 2:
The system extracts and transmits only the essential portions of video data—specifically, video clips containing detected events—rather than transmitting complete continuous video streams. This extraction approach maintains reliable surveillance coverage of important events while dramatically reducing the quantity of data transmitted and stored remotely.
3Area of stationary object
If unregulated cameras are deployed for surveillance, then coverage area expands, but security risks increase due to potential hacking
Solution Approach 1:
The system introduces a local recording device as an intermediary between unregulated cameras and the surveillance network. These recording devices act as trusted mediators that receive video feeds from unregulated cameras, perform local event detection and filtering, and only transmit processed data to remote servers. This intermediary layer isolates unregulated cameras from direct network access, reducing security vulnerabilities while maintaining expanded surveillance coverage.
4Speed
If continuous video streaming is provided to remote applications, then real-time surveillance is achieved, but network bandwidth consumption increases
Solution Approach 1:
The system transitions from continuous video streaming to periodic transmission of discrete video clips containing detected events. Instead of maintaining constant real-time video streams to remote applications, the system periodically transmits only those video segments where significant events occurred, reducing bandwidth consumption while providing timely notification and review capability for important surveillance events.
Data Source
AI summary
Disclosed are platforms, systems, methods, and media for performing networked video surveillance while preserving network bandwidth and performance by utilizing at least one video camera deployed on a quarantined sub-network, a local video recorder applying an edge computer vision algorithm to detect an area of an object above a threshold area, and one or more back-end applications applying one or more multi-stage hierarchical computer vision models to the uploaded recorded video to tag video with one or more identified classes of objects.


