Content-Aware Video Storage Reducing Bandwidth and Capacity Demands
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Solution Overview
Problem
Network camera systems face significant challenges with high storage and bandwidth requirements due to high-resolution video data, leading to costly infrastructure, limited scalability, and short operational lifespan of storage devices, as well as inadequate storage solutions that lack redundancy and long-term archiving capabilities.
Innovation Solution
Implementing a content-aware storage system within network cameras that uses video analytics to differentiate between event-of-interest and non-event-of-interest video data, storing high-quality data locally and reducing network bandwidth by streaming only managed amounts of video data, thereby reducing storage needs and extending device lifespan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If video data are recorded at full quality with high resolution and high frame rates, then video quality is improved, but storage capacity requirements increase significantly
Solution Approach 1:
The patent applies local quality by storing video data at different quality levels in different locations within the storage system. Full-quality video data is stored locally in camera-connected storage units, while lower-quality or summarized video data is stored in centralized storage facilities. This allows the system to maintain high video quality where needed while reducing overall storage capacity requirements through selective quality reduction for archival purposes.
2Ease of operation
If storage devices operate continuously to maintain video data availability, then data accessibility is improved, but device lifespan decreases due to wear and failure
Solution Approach 1:
The patent implements periodic action by cycling storage devices between active and standby states. When a storage device reaches a predetermined number of write operations or time threshold, it is automatically taken offline and replaced with a fresh device. The system periodically rotates through multiple storage devices, allowing each device to be replaced before failure while maintaining continuous data availability. This periodic replacement strategy extends the effective lifespan of the storage system while ensuring data accessibility.
3Quantity of substance
If centralized storage systems are used to manage large volumes of video data, then storage capacity is improved, but system complexity increases due to bandwidth and infrastructure requirements
Solution Approach 1:
The patent applies segmentation by dividing the storage system into multiple independent segments: camera-connected storage units, network storage devices, and centralized storage facilities. Each segment handles a portion of the video data independently, reducing the bandwidth and processing requirements of any single component. This segmented architecture allows the system to manage large volumes of video data while maintaining manageable complexity at each level of the hierarchy.
4Reliability
If storage devices are frequently replaced to prevent data loss, then data reliability is improved, but maintenance costs and operational overhead increase
Solution Approach 1:
The patent implements preliminary action by proactively replacing storage devices based on predetermined usage thresholds before actual failure occurs. The system monitors the number of write operations and operational time of each storage device, and automatically initiates replacement when thresholds are approached. This preliminary replacement strategy ensures data reliability by preventing data loss from unexpected failures, while reducing maintenance overhead by using automated monitoring and scheduling rather than reactive manual intervention.
Data Source
AI summary
Video analytics and a mass storage unit are contained in a camera housing of a video camera. The video analytics analyzes video data produced by the video camera and detects whether there is an occurrence of a defined event of interest. The video data representing the field of view of the scene observed by the video camera are stored in the mass storage unit. The stored video data include video data of first and second qualities. The first quality represents the occurrence in a field of view of the video camera of the defined event of interest detected by the video analytics. The second quality represents the nonoccurrence in the field of view of the defined event of interest detected by the video analytics. Storing video data of first and second qualities reduces system storage capacity demands.