CCTV Tube Ranking and Path Visualization for Multi-Camera Tracking
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Solution Overview
Problem
Conventional CCTV monitoring systems face challenges in accurately and efficiently tracking objects over large areas, seamlessly switching between multiple cameras, maintaining privacy, and integrating human and automated tracking systems, with limited visualization and analysis capabilities.
Innovation Solution
A method for displaying an object of interest in a CCTV monitoring system that includes receiving and processing video data from multiple cameras, generating tubes of similarity, and integrating them through a user interface to track and visualize paths, using deep learning for feature extraction and spatiotemporal verification, and allowing manual operator intervention for path confirmation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated tracking algorithms are used to track objects across multiple CCTV cameras, then tracking efficiency is improved, but accuracy decreases in complex environments
Solution Approach 1:
The system segments the tracking task into multiple stages: initial detection by automated algorithms, candidate tube generation, operator verification, and final path confirmation. This segmentation allows automated processing for efficiency while human judgment ensures accuracy in complex scenarios.
Solution Approach 2:
The system introduces an intermediary verification layer where operators review and confirm tracking results. This intermediary step acts as a bridge between automated tracking and final accurate path determination, resolving the contradiction by combining both automated efficiency and human accuracy.
2Measurement precision
If manual monitoring is used to track targets across multiple cameras, then tracking accuracy is maintained, but productivity decreases due to human fatigue
Solution Approach 1:
The system applies partial automation where AI algorithms perform initial tracking and generate candidate tubes, but operators perform the essential verification action. This partial automation maintains accuracy through human judgment while improving productivity by eliminating complete manual monitoring.
Solution Approach 2:
The automated algorithm performs self-service by generating tracking candidate tubes and presenting them for operator confirmation, reducing the workload on operators while maintaining their critical role in ensuring accuracy.
3Area of stationary object
If multiple CCTV cameras are integrated for wide-area tracking, then coverage area is improved, but system complexity increases
Solution Approach 1:
The system segments the multi-camera tracking problem into independent camera view analyses, where each camera generates its own candidate tubes. This segmentation simplifies the integration process by treating each camera independently while still achieving wide-area coverage through coordinated analysis.
Solution Approach 2:
The system creates a universal tracking framework that works across multiple camera views simultaneously. The same tracking algorithm and verification process apply to each camera, providing a multi-functional solution that handles wide-area coverage without proportionally increasing complexity.
4Measurement precision
If facial recognition is used for precise identification, then identification accuracy is improved, but device requirements and complexity increase
Solution Approach 1:
The system applies local quality by using facial recognition only when and where faces are clearly visible in the video feed, rather than forcing it on all tracking scenarios. This selective application maintains high identification accuracy when applicable while avoiding the complexity overhead in situations where facial recognition is not feasible.
Data Source
AI summary
According to an embodiment, there is provided a method for displaying an object of interest in a CCTV monitoring system, including a first operation of receiving, by the CCTV monitoring system, an object of interest to be searched, a second operation of acquiring, by the CCTV monitoring system, a plurality of tubes related to the object of interest from each camera of the CCTV monitoring system and providing the tubes through a first user interface, and a third operation of identifying, by the CCTV monitoring system, a search target based on a tube selected through the first user interface and generating and displaying a path based on the order in which the identified search target appears in each camera of the CCTV monitoring system, wherein, in the second operation, the plurality of tubes are displayed in order of highest to lowest similarity to the object of interest.