Unified Video Data Framework for Multi-Source Activity Monitoring
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Solution Overview
Problem
Current video data processing techniques are limited in their ability to aggregate and analyze data from multiple sources, particularly struggling with processing large amounts of streaming video data effectively and integrating diverse video inputs into a unified framework for scene analysis over time and space.
Innovation Solution
The Placemeter system processes video data from various sources, including security cameras, smartphones, and webcams, using novel algorithms to transform video signals into actionable data, combining measurements with lower resolution activity maps and weather information to infer activity levels in space and time, and provides a unified framework for geolocation and time-stamping data from different sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If video data from multiple diverse sources is aggregated and processed, then the quantity and variety of data available for analysis is improved, but the device complexity and processing difficulty increase
Solution Approach 1:
The system segments video processing into distinct modules: video data reception from multiple sources, activity detection module that identifies human activities, scene classification module that categorizes scenes, and a unified framework that integrates results. This modular segmentation allows complex multi-source video data to be processed through specialized components rather than a monolithic system.
Solution Approach 2:
The patent introduces a unified framework that acts as an intermediary layer between diverse video sources and the analysis components. This framework standardizes data from different sources (security cameras, smartphones, webcams) into a common format and coordinate system, enabling seamless integration without requiring complex source-specific processing logic.
2Measurement precision
If large amounts of streaming video data are processed, then the measurement precision and activity detection accuracy are improved, but the processing time and computational resources increase
Solution Approach 1:
The system extracts only the essential features and activities from video streams rather than processing all raw data. The activity detection module identifies key human activities and the scene classification module extracts relevant scene characteristics, discarding redundant information. This extraction approach maintains high detection accuracy while reducing processing time and computational load.
3Adaptability or versatility
If diverse video sources are integrated into a unified framework, then the adaptability and versatility of the system are improved, but the device complexity and integration difficulty increase
Solution Approach 1:
The unified framework is designed with universal interfaces and standardized processing pipelines that can handle multiple video sources (security cameras, smartphones, webcams) through a single system architecture. This multi-functionality allows the system to adapt to different source types without requiring separate integration pathways for each device type.
Data Source
AI summary
Embodiments of a method and system described herein enable capture of video data streams from multiple, different devices and the processing of the video data streams. The video data streams are merged such that various data protocols can all be processed with the same worker processors on different types of operating systems, which are typically distributed. An embodiment uses a mobile device (such as a mobile phone) as a device and deploys a video sensor application on the mobile device for encoding consecutive video files, time stamping the consecutive video files, and pushing the consecutive video files to a file server to produce a stable stream of video data. Thus avoiding the inefficiencies associated with having video processing in the data flow loop.