Video Behavior Analysis Using Keyword Anchored Focalized Visualizations
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Solution Overview
Problem
Current video management systems are monolithic, inefficient in scaling, and lack the ability to effectively detect events of interest and produce accurate video summarizations, particularly in scenarios with many locations and few cameras, and they fail to analyze human behavior in a focalized manner.
Innovation Solution
A system and method for extracting salient fragments from a video stream, associating time anchors with keywords, generating focalized visualizations, tagging human subjects, and analyzing behavior to generate behavior scores, allowing for meaningful assessments of behavior at specific instances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a monolithic video analytics architecture is used, then the system is simple to implement, but it cannot scale efficiently with increasing number of components or task complexity
Solution Approach 1:
The patent applies segmentation by dividing the monolithic video analytics architecture into multiple independent microservices. Each microservice handles a specific analytics task (e.g., object detection, tracking, classification) and can be independently deployed, scaled, and maintained. This modular structure enables the system to scale efficiently by adding or removing individual services based on task complexity and component needs, while maintaining overall system functionality.
2Adaptability or versatility
If traditional video surveillance systems are used, then the system covers few locations with many cameras, but it is inefficient for scenarios with many locations and few cameras
Solution Approach 1:
The patent implements a universal cloud-based analytics platform that serves multiple locations and diverse analytics needs through a single shared infrastructure. The system provides multi-functional capabilities including object detection, tracking, classification, and custom analytics tasks that can be applied across numerous locations with few cameras each. This universal approach eliminates the need for separate camera-heavy deployments at each location while maintaining high analytics efficiency through centralized processing.
3Measurement precision
If the system processes entire video streams, then comprehensive analysis is achieved, but it cannot efficiently detect specific events of interest or produce accurate video summarizations
Solution Approach 1:
The patent extracts and processes only the relevant portions of video streams based on detected events of interest. Instead of analyzing entire video streams continuously, the system identifies key events (such as specific object appearances, actions, or patterns) and extracts only those segments for detailed analysis and summarization. This extraction approach maintains high event detection accuracy by focusing computational resources on relevant moments while significantly reducing overall processing time.
4Measurement precision
If behavioral analysis is performed on entire video streams, then complete behavior patterns are captured, but it cannot provide meaningful behavioral measurements focalized on specific keywords or events
Solution Approach 1:
The patent applies local quality by providing different levels of behavioral analysis granularity tailored to specific needs. The system can perform comprehensive behavioral analysis across entire video streams when needed, but more commonly focuses behavioral measurements on specific keywords, events, or time periods of interest. This localized approach to behavioral analysis maintains measurement precision by concentrating analysis resources on relevant segments while reducing the overall data volume that requires processing.
Data Source
AI summary
A system and method for analyzing behavior in a video is described. The method includes extracting a plurality of salient fragments of a video; associating a time anchor with an utterance of a first keyword in an audio track associated with the video; generating a focalized visualization, based on the time anchor, from one or more of the plurality of salient fragments of the video; tagging a human subject in the focalized visualization with a unique identifier; and analyzing behavior of the human subject, using the focalized visualization, to generate a behavior score associated with the unique identifier and the first keyword.