Video Analytics Module for Early Camera Malfunction Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video camera systems lack effective methods for predicting and responding to camera malfunctions, leading to unexpected losses in video stream footage and recorded video.
Innovation Solution
A video camera system that includes a plurality of video cameras, a video recorder, and a video analytics module. The module analyzes video parameters such as frame lost ratio and frame rate to identify malfunctioning cameras, deactivates them, and adjusts other cameras to maintain coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If video cameras are operated continuously without monitoring, then productivity is maintained, but reliability deteriorates due to unexpected failures and data loss
Solution Approach 1:
The system performs preliminary analysis of video parameters (frame rate, packet loss ratio, bitrate) to detect early signs of camera malfunction before actual failure occurs. This allows proactive identification and replacement of failing cameras, preventing complete system failure and maintaining high reliability without requiring complex real-time intervention mechanisms
Solution Approach 2:
The system continuously monitors video parameters from cameras and provides feedback about camera health status. By analyzing trends in frame rate, packet loss, and bitrate, the system can identify deteriorating cameras and alert operators before complete failure, creating a feedback loop that maintains system reliability through early detection and response
2Reliability
If multiple video cameras are deployed to ensure coverage, then reliability improves through redundancy, but device complexity increases due to camera coordination and failure identification
Solution Approach 1:
The system implements continuous monitoring of video parameters from each camera and provides feedback about individual camera performance. By comparing metrics such as frame rate, packet loss ratio, and bitrate across multiple cameras, the system can automatically identify which specific camera is malfunctioning, making failure detection straightforward even in multi-camera deployments
Solution Approach 2:
The system automatically identifies and flags malfunctioning cameras through analysis of video parameters without requiring manual inspection or complex coordination protocols. The self-diagnostic capability allows the system to manage multiple cameras reliably while keeping the identification process simple and automated
3Reliability
If video parameters are continuously monitored to detect malfunctions, then reliability improves, but use of energy increases due to constant analysis
Solution Approach 1:
The system monitors video parameters continuously but applies full analysis only when anomalies are detected. Normal operation uses minimal processing energy, while intensive analysis is activated selectively when frame rate, packet loss, or bitrate deviate from expected ranges, achieving reliable failure detection without sustained high energy consumption
Solution Approach 2:
The system performs video parameter analysis at periodic intervals rather than continuously processing every frame. By sampling video parameters at appropriate intervals and analyzing trends over time, the system achieves reliable malfunction detection while significantly reducing energy consumption compared to continuous real-time analysis
Data Source
Figure 1
Figure 2
AI summary
A video camera system including: one or more video cameras; a video recorder in communication with each of the one or more video cameras; a video analytics module, the video analytics module being a computer program product embodied on a computer readable medium, the computer program product including instructions that, when executed by a processor, cause the processor to perform operations including: obtaining video parameters of a plurality of video frames received at the video recorder, the plurality of video frames being transmitted from the one or more video cameras to the video recorder; determining an abnormality within the video parameters; and identifying a malfunctioning video camera of the one or more video cameras that produced the abnormality within the video parameters.