Focalized Behavioral Video Analysis via Salient Fragment Extraction
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Solution Overview
Problem
Current video management systems are monolithic, inefficient in detecting events of interest, and lack the ability to produce accurate video summarizations, especially in scenarios with many locations and few cameras, and they fail to effectively analyze human behavior in a focalized manner.
Innovation Solution
A system and method for extracting salient fragments from a video stream, generating focalized visualizations based on time anchors, tagging human subjects, and analyzing their behavior to generate behavior scores, allowing for meaningful assessments of scenarios or tasks by focusing on relevant time instances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a monolithic video analytics architecture is used, then the system structure is simple, but the system cannot efficiently detect events of interest and produce accurate video summarizations
Solution Approach 1:
The patent divides the monolithic video analytics system into multiple independent microservices, each handling specific analytics tasks (e.g., object detection, tracking, behavior analysis). These microservices communicate through standardized interfaces, enabling modular processing that improves event detection accuracy while maintaining manageable system complexity through clear separation of concerns.
Solution Approach 2:
The patent introduces an event broker as an intermediary component that receives video streams, coordinates analytics tasks across multiple microservices, and aggregates results. This mediator pattern enables efficient event detection by routing queries to appropriate services and producing accurate video summarizations without requiring complex direct interactions between all system components.
2Adaptability or versatility
If video analytics are performed centrally, then the system is easy to manage, but the system cannot efficiently handle deployments with many locations and few cameras
Solution Approach 1:
The patent implements a dynamic architecture where analytics capabilities can be selectively enabled or disabled based on deployment needs. Microservices can be deployed independently at different locations, allowing the system to adapt to various scenarios (many locations with few cameras, or few locations with many cameras) while maintaining centralized coordination through the event broker for manageable operations.
Solution Approach 2:
The patent creates universal microservices that can function in multiple deployment scenarios. The same analytics services can handle both centralized and distributed configurations, adapting to different camera densities and location counts without requiring separate system designs, thus achieving both versatility and ease of management.
3Measurement precision
If the entire video stream is analyzed, then comprehensive behavior analysis is achieved, but the processing time and computational resources increase significantly
Solution Approach 1:
The patent extracts and analyzes only the most relevant portions of video streams by using object detection to identify subjects of interest, then applying behavior analysis specifically to these extracted regions. This selective approach maintains comprehensive behavior analysis precision for relevant subjects while significantly reducing processing time by ignoring irrelevant video content.
Solution Approach 2:
The patent applies behavior analysis at different levels of detail depending on the situation - using full analysis only when necessary (e.g., when suspicious behavior is detected) and simplified analysis for routine monitoring. This partial action approach maintains measurement precision for critical events while reducing overall processing time through selective application of computational resources.
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; generating a focalized visualization, based on a 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 time anchor.