Surveillance Camera Image Ranking via Machine Learning Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Surveillance camera systems lack the ability to anticipate and prioritize image data of interest for users effectively, relying on user manual selection and lacking a robust feedback mechanism to improve data relevance over time.
Innovation Solution
A surveillance camera system that utilizes machine learning to create user-specific models based on request data, video primitives, and user feedback, ranking and suggesting image data of potential interest, and incorporating voting mechanisms to update models dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the system provides all available image data to users, then users can access complete surveillance data, but users cannot efficiently locate data of interest and information overload occurs
Solution Approach 1:
The system performs self-service by automatically analyzing user behavior patterns and generating personalized image data recommendations without requiring manual user input. The machine learning model continuously learns from user interactions and autonomously improves data ranking, eliminating the need for users to manually search or filter through surveillance footage.
Solution Approach 2:
The system implements feedback mechanisms by collecting user interactions with recommended image data and using this information to continuously update and refine the machine learning model. This feedback loop enables the system to learn from user preferences and improve the relevance of recommended data over time, creating a dynamic adaptation to individual user needs.
2Reliability
If the system uses simple ranking methods, then implementation is easier and faster, but the system cannot accurately anticipate user interests
Solution Approach 1:
The system performs preliminary action by pre-processing and analyzing user interaction data to build comprehensive user profiles before actual image data retrieval occurs. The machine learning model is trained in advance on historical data to establish baseline user preferences, enabling faster and more accurate real-time recommendations when users access the system.
Solution Approach 2:
The system embraces dynamics by implementing a machine learning model that continuously adapts and evolves based on changing user preferences and behaviors. Rather than using static ranking rules, the model dynamically adjusts its parameters and predictions based on new interaction data, allowing it to accurately capture evolving user interests while maintaining manageable complexity through iterative learning.
3Measurement precision
If the system collects extensive user interaction data, then model accuracy improves, but user privacy concerns increase and data storage requirements grow
Solution Approach 1:
The system applies the extraction principle by isolating and focusing only on the most critical interaction features that drive user preference predictions. Rather than storing and processing all raw interaction data, the model extracts essential patterns and characteristics (such as viewing duration, selection frequency, and scroll behavior) to build compact user profiles that maintain high prediction accuracy while minimizing data storage requirements.
Data Source
AI summary
A system and method for modeling and distributing image data of interest to users is disclosed. Users on user devices such as mobile phones send request messages for image data captured by surveillance cameras of the system. The request messages include information for selecting the image data, such as camera number and time of recording of the image data, in examples. In response, an application server of the system collects the image data from the surveillance cameras, and supplies image data to the users based on a model that the application server creates and updates for each of the users. The model ranks image data of potential interest for each of the users, where the model is based on the information for selecting the image data provided by the users. Preferably, a machine learning application of the application server creates the model for each of the users.