Intelligent Monitoring System with Object Segmentation
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Solution Overview
Problem
Conventional systems for monitoring environments lack effective orchestration and coordination tools, failing to provide granular insights for timely decision-making and are not adaptable across various applications such as agriculture, aviation, and public safety.
Innovation Solution
A system comprising one or more processors and a memory that obtains image information, displays it, identifies discrete objects, and classifies them based on object templates, further generating graphics for overlay display and tracking location, path, and heat signature information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional monitoring systems are used, then basic surveillance is achieved, but granular insights and situational awareness are insufficient
Solution Approach 1:
The system segments the monitoring environment into discrete objects, each with unique identifiers and attributes. Objects are further segmented into hierarchical categories (e.g., vehicle types, animal species) enabling granular analysis while maintaining overall situational context through the parent-child relationship structure.
Solution Approach 2:
The system adds temporal and spatial dimensions to object monitoring by tracking object trajectories over time and organizing objects into hierarchical taxonomies. This multi-dimensional approach transforms basic surveillance into comprehensive situational awareness by analyzing objects across multiple attribute spaces simultaneously.
2Adaptability or versatility
If multiple monitoring sources are integrated, then comprehensive coverage is achieved, but coordination and orchestration become complex
Solution Approach 1:
The system implements a universal object model that can represent entities from diverse monitoring sources (cameras, sensors, RFID) using a common framework. All objects share the same attribute structure and relationship model, enabling seamless integration of multiple sources without requiring source-specific processing logic.
Solution Approach 2:
The system introduces an intermediary object registry that mediates between multiple monitoring sources and the analysis engine. This registry standardizes object representations and manages cross-source object relationships, simplifying the orchestration complexity by providing a single point of coordination.
3Speed
If real-time monitoring is implemented, then timely decision-making is enabled, but computational resources are consumed
Solution Approach 1:
The system applies partial processing by focusing computational resources on objects of interest rather than analyzing all detected objects uniformly. The hierarchical object model enables selective deep analysis of specific object categories while using lighter processing for others, reducing overall computational energy consumption.
Solution Approach 2:
The system performs preliminary object detection and classification at the source level before transmitting detailed data for further analysis. This pre-processing step filters out irrelevant information early in the pipeline, reducing the computational burden on centralized systems while maintaining real-time responsiveness for critical objects.
Data Source
AI summary
Systems and methods are provided for intelligently monitoring environments, classifying objects within such environments, detecting events within such environments, receiving and propagating input concerning image information from multiple users in a collaborative environment, identifying and responding to situational abnormalities or situations of interest based on such detections and/or user inputs.