Surveillance Traffic Optimizer for I-Frame Network Overload
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Solution Overview
Problem
Existing video surveillance systems face issues with network overload and video frame corruption due to the concurrent arrival of high-definition I-frames, leading to video jitter, corruption, and increased latency, particularly in systems using power over Ethernet cameras.
Innovation Solution
A video processing system with a traffic optimizer that monitors I-frame arrivals and adjusts the I-frame generation time of cameras to prevent overload, using threshold values to manage the transmission of I-frames and synchronize camera intervals to maintain data integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high-definition video surveillance is implemented, then video quality is improved, but network overload and video frame corruption occur
Solution Approach 1:
The system implements periodic transmission of I-frames at predetermined time intervals rather than continuous or event-triggered transmission. The traffic optimizer monitors and adjusts the time interval between I-frames from each camera to ensure they are transmitted at staggered intervals, preventing network overload while maintaining high-definition video quality.
2Quantity of substance
If multiple cameras transmit I-frames simultaneously, then data completeness is improved, but network overload and frame corruption increase
Solution Approach 1:
The system segments the I-frame transmission from multiple cameras by assigning different time intervals to each camera. The traffic optimizer divides the network traffic into separate time slots for each camera, ensuring that I-frames are transmitted at staggered intervals rather than simultaneously, thereby preventing network overload while maintaining data completeness.
3Stability of the object's composition
If I-frame transmission frequency is increased, then video stability is improved, but latency and network overload increase
Solution Approach 1:
The system dynamically adjusts the I-frame transmission time intervals based on real-time network conditions and camera performance. The traffic optimizer continuously monitors the system and modifies the time intervals between I-frames from each camera, finding the optimal balance between video stability and latency by making the transmission schedule adaptive rather than fixed.
Data Source
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AI summary
A system including a surveillance system that receives compressed video from a plurality of network video cameras and a traffic control subsystem of the surveillance system further including a first processor of the subsystem that monitors for and detects the number of I-frames per time period received from each of the plurality of video cameras, a second processor of the subsystem that compares the number of received I-frames with a threshold value and detects that one of the plurality of network cameras has exceeded the threshold value and a third processor that sends a control message to the one of the plurality of network cameras adjusting a time interval of I-frames from the one camera based upon the comparison.