Bandwidth Allocation for Video Surveillance Systems
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Solution Overview
Problem
Current video surveillance systems face scalability and cost challenges due to the increasing bandwidth and computational power required as the number of video sources grows, making it difficult to effectively monitor large areas without detecting critical events.
Innovation Solution
A distributed video surveillance system that allocates bandwidth dynamically based on event/object detection accuracy, threat level, location importance, and network conditions, using computer vision algorithms and dynamic bandwidth allocation protocols to optimize image/video quality and reduce unnecessary data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If additional video sources are deployed to increase surveillance coverage, then the monitoring area expands, but the required bandwidth and computational power increase
Solution Approach 1:
The patent extracts only the essential features from video streams for transmission. Instead of transmitting complete high-resolution video feeds from all cameras, the system processes video locally and extracts only relevant features (such as object presence, motion patterns, or detected events) for transmission to the central server. This dramatically reduces bandwidth requirements while maintaining surveillance coverage.
Solution Approach 2:
The patent applies local quality by performing video processing and feature extraction at distributed edge devices or local servers near each camera. Each location processes its own video streams independently, extracting only necessary information locally before transmission. This distributes computational load and reduces the bandwidth burden on the central system, allowing expanded coverage without proportional increases in central processing resources.
2Area of stationary object
If more video sources are added to monitor larger areas, then surveillance coverage improves, but system cost increases
Solution Approach 1:
The patent employs inexpensive cameras and edge processing devices that can be deployed widely across the surveillance area. Rather than using a few expensive high-end cameras with centralized processing, the system uses many lower-cost units with local processing capabilities. This approach reduces the marginal cost of adding additional surveillance points while maintaining overall system effectiveness.
Solution Approach 2:
The patent replaces expensive centralized mechanical processing systems with distributed electronic processing at the edge. By implementing computer vision algorithms and feature extraction locally at each camera or small group of cameras, the system eliminates the need for costly high-bandwidth infrastructure and centralized processing power, thereby reducing overall system cost while enabling expanded coverage.
3Measurement precision
If high image quality is maintained for all video sources, then detection accuracy is preserved, but bandwidth consumption increases
Solution Approach 1:
The patent extracts only the essential visual features needed for detection tasks from full-resolution video streams. Instead of transmitting complete high-quality images, the system processes video locally and extracts only relevant features (such as object boundaries, motion vectors, or detected event characteristics) for transmission. This maintains detection accuracy by preserving critical information while dramatically reducing bandwidth consumption.
Solution Approach 2:
The patent applies partial action by transmitting only the portion of video information that is necessary for detection purposes. Rather than sending complete video feeds, the system transmits selectively processed feature data that contains just enough information for accurate object detection and event recognition. This partial transmission approach maintains detection accuracy while minimizing bandwidth usage.
Data Source
AI summary
A video surveillance system includes a plurality of video sources, at least one processing server in communication with the video sources, and at least one monitoring station viewing video from the video sources. The video sources are distributed over a target area to be monitored by the surveillance system and are attached to a video network. The processing server allocates bandwidth to the video sources with an accuracy function which provides event/object detection accuracy as a function of image/video quality transferred over the network. The image accuracy is the accuracy with which the processing server identifies features in a video image. Bandwidth is allocated by optimizing the overall event/object detection accuracy by adjusting image/video quality for each video source optionally subject to the dynamic network conditions experienced by each video source.