Video Fingerprinting via Row-Column Analysis for Portable Monitoring
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Solution Overview
Problem
Current video monitoring systems face high costs and inefficiencies due to redundant video feeds and infrequent motion events, requiring high computation capacity and complex algorithms for automatic frame analysis in portable devices.
Innovation Solution
A method and system for generating transformed representations of video data by analyzing statistical values of pixel properties in rows and columns, combining these to identify frames of interest, using a processor and memory-based device for efficient video analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex algorithms with high computation capacity are used for automatic frame analysis in portable devices, then frame identification accuracy is improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent divides the video frame analysis into multiple passes: a first pass using a first algorithm to identify candidate frames, and a second pass using a second algorithm to refine identification. This segmentation allows portable devices to achieve higher accuracy without requiring the full computational power of complex algorithms to run continuously.
Solution Approach 2:
The patent applies a simplified first algorithm to all frames initially, then applies the more computationally intensive second algorithm only to candidate frames identified in the first pass. This partial application of the more complex algorithm reduces overall computational requirements while maintaining identification accuracy.
2Reliability
If continuous monitoring of video feeds is performed, then detection sensitivity is improved, but monitoring costs and resource consumption increase
Solution Approach 1:
The patent implements periodic analysis of video frames using the transformed representation, rather than continuous full-frame analysis. The system periodically generates frame representations and combines them into transformed representations that capture motion and changes, enabling continuous monitoring capability with reduced resource consumption.
Solution Approach 2:
The patent creates a transformed representation (a simplified copy or abstraction) of the video data that preserves key information about motion and changes. This transformed representation uses fewer resources to analyze while maintaining detection sensitivity, as it captures essential features without requiring full frame processing.
3Loss of information
If manual review of entire video recordings is performed, then analysis completeness is improved, but time consumption and productivity decrease
Solution Approach 1:
The patent extracts key information from video frames by generating transformed representations that highlight motion and changes. This extraction process creates a condensed summary representation that preserves essential information about events in the video, allowing users to identify frames of interest without reviewing entire recordings.
Solution Approach 2:
The patent performs preliminary analysis to generate transformed representations and identify candidate frames of interest before user review. This preliminary action pre-processes the video data to highlight important segments, enabling users to quickly jump to key segments without manual review of entire recordings.
Data Source
AI summary
A system used for generating a transformed representation of a quantity of video data structured as a plurality of frames including arrays of rows and columns of pixels having pixel properties. The system may generate first representations of the video data based on a plurality of the rows; generate second representations of the video data based on a plurality of the columns; generate frame representations corresponding to the frames and based on the first and second representations; and combine the frame representations to form the transformed representation of the video data. The system may also generate frame representations respectively corresponding to the frames; combine the frame representations to form a transformed representation of the video data; analyze the transformed representation; and identify frames of interest based on the analysis.