Scene-Aware Video Surveillance Detection Model Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Surveillance systems face challenges in efficiently differentiating between vehicles, animals, and people, leading to unnecessary alerts and increased costs due to false alarms, as existing detection models are not tailored to specific scenes and require significant processing power.
Innovation Solution
A detection model optimization system that receives imaging data from surveillance cameras, generates site-specific models using annotated training data, and selects optimized models based on performance parameters like precision, recall, and cost analysis, utilizing convoluted neural networks and deep-learning classifiers to reduce false positives and improve response times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If generic detection models are used for surveillance, then device complexity is reduced, but detection precision deteriorates leading to false alarms
Solution Approach 1:
The system generates scene-specific detection models tailored to each surveillance location's unique characteristics. By analyzing imaging data from each scene and training custom models on locally-relevant objects and events, the system achieves high detection precision without requiring universally complex models. Each scene receives a customized model that focuses on local patterns, resolving the contradiction between accuracy and complexity.
Solution Approach 2:
The system performs preliminary scene analysis and model training during an initialization phase before actual surveillance begins. By pre-processing imaging data, identifying scene-specific objects and events, and training detection models in advance, the system prepares optimized models that reduce false alarms from the start. This preliminary action eliminates the need for complex real-time adjustments while maintaining high detection accuracy.
2Measurement precision
If custom-trained detection models are deployed for each scene, then detection precision improves, but processing power requirements increase
Solution Approach 1:
The system extracts and removes irrelevant information from scenes by identifying and excluding known spurious motion sources. By separating useful detection targets from background noise and irrelevant objects, the model focuses processing power only on meaningful detections. This extraction approach maintains high detection accuracy while significantly reducing the computational burden during actual surveillance operations.
Solution Approach 2:
The system creates simplified copies of detection models that are optimized for specific scene characteristics. Rather than deploying full-complexity models to all devices, lightweight scene-specific versions are generated and deployed to individual surveillance points. These copied models maintain local detection accuracy while requiring minimal processing power, resolving the contradiction between precision and energy consumption.
3Adaptability or versatility
If scene-specific models are generated, then adaptability to different environments improves, but device complexity increases
Solution Approach 1:
The system automatically adapts to new scenes by self-training detection models using imaging data from each specific location. Rather than requiring manual configuration or complex pre-programming for each environment, the system autonomously analyzes local objects, events, and patterns, then generates appropriate detection models. This self-service approach achieves high scene adaptability while keeping the deployment process simple and the operational complexity low.
Data Source
AI summary
Methods, and systems including computer programs encoded on a computer storage medium, for training a detection model for surveillance devices using semi-supervised learning. In one aspect, the methods include receiving imaging data collected by a camera of a scene within a field of view of the camera. Annotated training data is generated from the imaging data and one or more detection models are trained using the annotated training data. Based on a set of performance parameters, an optimized detection model is selected of the one or more detection models, and the optimized detection model is provided to the camera.