Distributed Video Analytics Architecture for Large Scale Processing
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Solution Overview
Problem
Current technologies face challenges in efficiently processing and analyzing large amounts of unstructured video data from sources like security cameras, requiring manual review and tagging, which is labor-intensive and inefficient for extracting meaningful information.
Innovation Solution
A distributed video analytics system that automatically recognizes and processes standard video file formats, converts them into structured information using MapReduce architecture and image processing algorithms, allowing for scalable analysis and retrieval of video data through SQL queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review and tagging is used to locate content of interest in video files, then content can be identified, but labor intensity and processing time increase significantly
Solution Approach 1:
The patent replaces manual mechanical review and tagging processes with automated computer-based video analytics processing. The system uses automated algorithms to analyze video content, extract meaningful information, and generate structured data without human intervention, thereby maintaining content identification accuracy while dramatically increasing processing throughput.
Solution Approach 2:
The patent introduces an intermediary layer of automated video analytics processing between the raw video data and the final content identification. This intermediary system processes video files through multiple stages including decoding, frame extraction, object detection, and structured data generation, enabling scalable automated content identification without manual labor.
2Ease of manufacture
If video content is stored as unstructured data in standards-based video file formats, then storage is simple, but advanced data analytics processing becomes difficult
Solution Approach 1:
The patent performs preliminary action by automatically processing video files and generating structured data representations before analytics processing is needed. The system pre-extracts meaningful information from unstructured video content, creating organized structured data that can be easily queried and analyzed, thereby maintaining storage simplicity while enabling advanced analytics.
Solution Approach 2:
The patent segments video content into structured data elements through automated processing. The system divides unstructured video streams into discrete analyzable units, extracting objects, events, and attributes into organized structured data formats, making the data suitable for advanced analytics while preserving the simplicity of standard video file storage.
3Loss of information
If manual tagging is used to generate metadata, then content can be described, but the process is labor-intensive and inefficient
Solution Approach 1:
The patent replaces manual tagging operations with automated computer-based video analytics processing. The system automatically generates comprehensive metadata by analyzing video content through algorithms that detect objects, track movements, identify events, and extract attributes, thereby maintaining metadata completeness while eliminating the time loss associated with manual tagging.
Data Source
AI summary
A video file is split into a plurality of chunks. At least a subset of the chunks is processed in parallel, including by detecting one or more moving objects and computing for each detected moving object a visual key and an associated attribute value. The visual key and the associated attribute value are provided as output.