Video Analytics Microservices for Scalable Human Behavior Detection
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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 and meaningful manner.
Innovation Solution
A system and method for extracting salient fragments from a video stream, associating time anchors with machine events, generating focalized visualizations, tagging human subjects, and analyzing their behavior to generate behavior scores, allowing for focused analysis of human behavior at relevant time 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 divides the monolithic video analytics system into modular microservices that can be independently deployed, scaled, and managed. Each microservice handles a specific analytics function (e.g., object detection, tracking, classification), allowing the system to scale by adding or removing individual services based on workload requirements without redesigning the entire architecture.
Solution Approach 2:
The patent creates a universal video analytics platform where a single infrastructure supports multiple analytics tasks through configurable microservices. The system can handle diverse analytics functions (intrusion detection, people counting, behavior analysis) using the same core components, enabling one system to serve multiple purposes across different deployment scenarios.
2Adaptability or versatility
If traditional video management systems are used, then the system covers few locations with many cameras, but it fails to serve deployments with many locations and few cameras
Solution Approach 1:
The patent shifts the scaling dimension from horizontal (adding more cameras at single locations) to vertical (adding multiple location nodes to the network). The cloud-based microservices architecture enables the system to scale across geographic locations by distributing analytics processing to edge devices at each location while maintaining centralized coordination, thus serving many locations with few cameras per location.
3Productivity
If connection-centric video distribution is used, then video routing is straightforward, but the system lacks efficient event detection and video summarization capabilities
Solution Approach 1:
The patent implements preliminary analytics processing at the edge devices and camera systems before video streams reach the central platform. Event detection, object tracking, and preliminary classification are performed locally, with only relevant events and summarized data transmitted to the cloud. This reduces bandwidth requirements and enables faster event response times while maintaining simple video routing for non-event periods.
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 occurrence of a first machine event of a machine operated by a human subject; generating a focalized visualization, based on the time anchor, from one or more of the plurality of salient fragments of the video; tagging the 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 machine event.