Unified Video Processing 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, struggling to effectively process large amounts of streaming video data and detect objects across diverse video inputs.
Innovation Solution
A system and method that processes video data from multiple sources, including security cameras, smartphones, and webcams, using a unified framework to unify geolocation and time references, and employs algorithms for object detection, classification, and data analytics to provide insights on environmental activity levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If video data is processed from multiple sources using a unified framework, then data aggregation capability is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal video processing framework that can handle multiple video sources (security cameras, smartphones, webcams) through a single unified system. The framework provides common functionalities for geolocation, time reference unification, and activity analysis that work across all input sources, eliminating the need for separate processing systems for each source type.
Solution Approach 2:
The patent introduces intermediary components including a backend subsystem that acts as a mediator between diverse video sources and the analysis engine. This backend subsystem standardizes data from different sources before processing, and the patent also uses intermediate data structures like activity contours and segmented regions to bridge raw video data with higher-level activity recognition.
2Loss of information
If large amounts of streaming video data are processed, then analysis comprehensiveness is improved, but processing efficiency deteriorates
Solution Approach 1:
The patent divides video processing into distinct segmentation stages: frame segmentation to identify activity areas, temporal segmentation to create activity contours over time, and spatial segmentation to define regions of interest. This multi-level segmentation reduces the computational burden by processing only relevant portions of the video data at each stage rather than analyzing all pixels in all frames.
Solution Approach 2:
The patent applies partial processing by focusing computational resources on detecting and analyzing only the portions of video data that contain meaningful activity. The system identifies activity areas and contours, then concentrates analysis on these segmented regions rather than processing the entire video stream uniformly, thereby achieving comprehensive activity analysis with reduced computational overhead.
Data Source
AI summary
Embodiments of a method and system described herein enable capture of video data streams from multiple, different video data source 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. In an embodiment the multiple video data sources comprises at least one mobile device executing a video sensing application that produces a video data stream for processing by video analysis worker processes. The processes include automatically detecting features in an urban scene comprising sidewalks, roads, bike routes and road crossings.