Event Detection via String Pattern Recognition in Video Surveillance
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Solution Overview
Problem
Current automated video surveillance systems face challenges in accurately monitoring human activities and behaviors due to inefficiencies in human review, high false alert rates, and poor reliability in real-world environments with clutter and distractions, leading to ineffective compliance monitoring in contexts like retail losses from non-compliant transactions.
Innovation Solution
A method employing string pattern recognition to classify events by assigning text labels to image primitives in a time-ordered set of training images and transactions, using a processing unit to discover positive and negative subset string patterns, and classify primitives as true or false scans based on voting systems and pattern matching, effectively distinguishing compliant from non-compliant transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated video analysis systems are implemented, then productivity and efficiency are improved, but measurement precision and reliability deteriorate due to false alerts and missed event recognitions
Solution Approach 1:
The patent segments video frames into primitive images and divides transactions into discrete time-ordered sequences. Each primitive image is independently analyzed and labeled, then combined into transaction-level patterns. This segmentation allows the system to process complex video data into manageable units while maintaining detection precision through systematic analysis of each segment.
Solution Approach 2:
The system performs preliminary actions by pre-processing video data into primitive images, pre-labeling transactions with start/entry/ending markers, and pre-discovering positive subset string patterns from training data. These preliminary preparations enable the system to efficiently and accurately detect events in production data without requiring complex real-time analysis, thus improving both productivity and measurement precision.
2Measurement precision
If human review of video feeds is used, then measurement precision is improved, but productivity and loss of time worsen due to time-consuming analysis
Solution Approach 1:
The system performs self-service by automatically analyzing video data through computational algorithms. The processing unit independently labels primitives, discovers patterns, and detects events without requiring human intervention. This automation maintains measurement precision through systematic pattern matching while dramatically improving productivity by eliminating time-consuming manual review processes.
Solution Approach 2:
The patent replaces the mechanical human review process with an automated computational system. Video analysis is substituted from manual visual inspection to algorithmic processing that uses string pattern recognition, voting systems, and pattern matching. This substitution maintains accuracy through rigorous computational methods while improving efficiency by processing data at machine speed rather than human speed.
3Measurement precision
If automated systems are used to reduce false alerts, then measurement precision is improved, but device complexity worsens due to sophisticated pattern recognition requirements
Solution Approach 1:
The system reduces complexity by segmenting the pattern recognition task into discrete, manageable steps: primitive image extraction, transaction labeling, pattern discovery from training data, and event detection in production data. This segmentation allows complex pattern recognition to be achieved through simple, systematic operations rather than a single complex algorithm, improving measurement precision while controlling device complexity.
Solution Approach 2:
The system performs preliminary pattern discovery from training data to create a reference database of positive and negative patterns. This pre-computation simplifies the actual event detection process, as the system only needs to match against pre-discovered patterns rather than perform complex real-time analysis. This preliminary action reduces device complexity during production while maintaining high measurement precision through established pattern matching.
Data Source
AI summary
Events are classified through string pattern recognition. Text labels are assigned to image primitives in a time-ordered set of training images and to related time-ordered transactions in an associated training transaction log in a combined time-ordered training string of text labels as a function of image types. Transactions are labeled in a training transaction log with a transaction label, a training primitive image of a start of a transaction with a start image text label, a training primitive of an entry of a transaction into the log with an entry image text label, and a training primitive of a conclusion of a transaction with an ending image text label. Positive subset string patterns are discovered representing true events from the combined time-ordered training string of text labels, and negative subset string patterns defined by removing single transaction primitive labels from the positive subset string patterns.