Distributed Video Analytics Segmentation for Scalable Event Summarization
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Solution Overview
Problem
Current video management systems are monolithic, difficult to scale, and lack efficient event detection and video summarization capabilities, particularly in deployments with many locations and few cameras, and they do not effectively provide focalized visualizations of events or subjects of interest.
Innovation Solution
A data-centric distributed system that decomposes video streams into segments, assigns metadata, and uses a middleware server to manage and distribute these segments across a network, allowing for scalable analytics and focalized visualizations based on user queries, enabling efficient detection of events and summarization of video content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a monolithic video analytics architecture is used, then video streams can be distributed and analyzed, but the system becomes difficult to scale and maintain as complexity increases
Solution Approach 1:
The patent divides the monolithic video analytics system into separate functional modules: video stream reception module, segment identification module, metadata generation module, and visualization generation module. Each module performs a specific task independently, allowing the system to scale by adding or removing modules without increasing overall complexity.
Solution Approach 2:
The patent introduces an intermediary layer that generates and manages metadata about video segments. This metadata layer acts as a mediator between the video stream processing and the visualization generation, decoupling these functions and enabling independent scaling of each component.
2Productivity
If traditional video management systems are used, then surveillance can be performed, but they lack efficient event detection and video summarization capabilities
Solution Approach 1:
The patent performs preliminary actions by identifying and segmenting video streams into meaningful units before detailed analysis. Metadata is generated in advance about each segment's characteristics, enabling efficient event detection and accurate summarization without losing important video information.
Solution Approach 2:
The patent extracts key information from video streams by identifying salient segments and generating metadata that captures essential event characteristics. This extraction process enables efficient event detection while maintaining summarization accuracy by focusing on the most relevant video portions.
3Measurement precision
If video streams are processed in full detail, then comprehensive analysis is achieved, but computational and network resources are excessively consumed
Solution Approach 1:
The patent segments video streams into distinct units and generates metadata that captures essential information about each segment. This segmentation allows the system to process only relevant segments in detail while using metadata for quick filtering and analysis of other segments, reducing computational resource usage while maintaining analysis accuracy.
Solution Approach 2:
The patent applies partial action by processing video segments selectively based on their relevance to events of interest. Instead of processing all video data in full detail, the system uses metadata to identify and thoroughly analyze only the necessary segments, achieving accurate analysis with reduced computational resources.
Data Source
AI summary
The disclosure includes a system and method for creating, storing, and retrieving a focalized visualization related to a location, an event or a subject of interest. A visualization server receives a query for creating a visualization from a client device, identifies and retrieves one or more segments of a video stream satisfying the query, and generates the visualization based on the one or more segments of the video stream.