Active Camera Tracking Across 360-Degree Next-Up Camera Sets
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Solution Overview
Problem
Current emergency dispatch systems face challenges in efficiently tracking moving vehicles or persons across a network of cameras due to the need for extensive human attention and the limitations of existing technology, leading to inadequate information for responders and potential safety risks.
Innovation Solution
A cloud-based dispatch system that integrates and processes live video and data streams to generate a map-based interface, enabling efficient tracking of moving objects by determining the locations and orientations of cameras and providing a 360-degree view of available cameras, allowing seamless transition between camera feeds as the object moves.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a grid-based view with multiple live camera feeds is used to track a moving object, then the ability to monitor multiple cameras simultaneously is improved, but human attention fatigue increases and detection effectiveness decreases
Solution Approach 1:
The system segments the complex task of multi-camera monitoring by automatically dividing cameras into hierarchical groups (primary, secondary, tertiary) based on their spatial relationships and relevance to the tracked object. This segmentation allows the dispatcher to focus on one primary camera feed while the system manages the complexity of multiple feeds in the background.
Solution Approach 2:
The system introduces an intelligent intermediary layer that automatically analyzes camera feeds, determines object presence, and manages camera switching. This intermediary processing layer between the raw camera feeds and the dispatcher reduces the cognitive load by pre-processing and organizing information before presentation to the human operator.
2Area of stationary object
If a dispatcher manually monitors multiple camera feeds to track a moving object, then comprehensive coverage is improved, but response time decreases due to the need for human analysis
Solution Approach 1:
The system performs preliminary actions by automatically pre-analyzing camera feeds to determine which cameras are most likely to contain the tracked object. It pre-establishes the hierarchy of cameras (primary, secondary, tertiary) and pre-processes the video feeds for object detection, so that when the dispatcher needs information, the analysis is already complete or near-complete.
Solution Approach 2:
The system replaces the mechanical process of manual human monitoring with automated electronic image processing and analysis. Computer vision algorithms automatically detect objects in video feeds, determine camera relevance, and manage the monitoring process, substituting human cognitive processing with automated computational processing that is faster and more consistent.
3Ease of manufacture
If existing camera networks are used without hardware overhauls, then implementation cost is reduced, but tracking capability across geographic areas is limited
Solution Approach 1:
The system achieves multi-functionality by enabling existing single-purpose security cameras to serve multiple functions: traditional security monitoring and active object tracking. The software platform can work with any standard video camera feed, allowing the same hardware infrastructure to support both security surveillance and dynamic tracking of moving objects across geographic areas.
Solution Approach 2:
The system changes the operational parameters of existing cameras by dynamically adjusting which cameras are active, their grouping relationships, and their prioritization based on real-time object location and movement. This allows the static hardware to adapt to dynamic tracking needs through software-controlled parameter changes rather than physical reconfiguration.
Data Source
AI summary
A crime center system generating a graphical user interface, which may be map-based, that facilitates efficient following of a moving object across cameras covering a geographic area. The system processes information on a network of cameras to determine the locations the cameras and their orientations. An object-tracking interface is generated by the system that includes an active camera view window presenting video from a camera capturing video of the geographic area that includes the tracked object. The system determines a set of the cameras, based on known locations and orientations, capturing images about the geographic space captured by the active camera. The system generates a “halo” of camera icons in the object-tracking interface around the periphery of the active camera view window, which provides a 360-degree view of available cameras about the active camera's location allowing a user to select a next active camera to efficiently track an object.