Surveillance Video Analysis With Operator Feedback Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing behavior recognition systems in surveillance cameras lack the ability for operator intervention during operation, leading to a lack of improvement in analytical accuracy.
Innovation Solution
A video processing apparatus and system that includes a video analyzer, display controller, and learning data accumulator, allowing operators to set category information through a category setting screen, and accumulate this information for learning processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If behavior recognition is performed by machine learning with predetermined behavior characterization, then the system can automatically recognize behaviors without prior learning, but the analytical accuracy cannot be improved during actual operation
Solution Approach 1:
The system enables operators to directly intervene and provide correction information during actual operation. The video analyzer uses this operator input to perform self-learning and improve detection accuracy continuously, transforming the system from a static machine learning model to a dynamic self-improving system that serves itself through operator feedback.
Solution Approach 2:
The system implements a feedback mechanism where operator corrections and category information are fed back to the video analyzer. This feedback loop allows the system to learn from actual operational data and continuously refine its behavior recognition algorithms, resolving the contradiction between automation and accuracy improvement.
2Productivity
If a behavior analysis system operates without operator intervention, then the system maintains continuous automatic operation, but discriminator learning cannot be performed
Solution Approach 1:
The system transitions from a static automatic operation mode to a dynamic hybrid mode that adapts based on operator needs. Operators can intervene at any point during continuous operation to provide learning data, and the system dynamically adjusts its detection parameters and categories based on this input, maintaining both continuous operation and learning capability.
Solution Approach 2:
The video analyzer is designed to perform multiple functions: automatic behavior recognition, operator interface for category setting, and continuous learning from operator input. This multi-functionality allows the system to maintain continuous operation while simultaneously accepting and processing operator feedback for discriminator learning.
3Measurement precision
If operator intervention is enabled for category setting, then learning data can be accumulated for improved accuracy, but the system complexity increases
Solution Approach 1:
The system introduces a category information setting screen as an intermediary interface between operators and the video analyzer. This mediator simplifies the operator's task to just selecting from predefined categories rather than directly programming complex detection parameters, reducing the perceived complexity while still enabling accurate learning data collection.
Solution Approach 2:
The system pre-defines behavior categories and detection parameters before operation begins. Operators only need to select from these pre-prepared options rather than creating detection logic from scratch, which simplifies the interface and reduces system complexity while still allowing custom learning data accumulation.
Data Source
AI summary
A video processing apparatus includes a video analyzer that analyzes video data captured by a surveillance camera, detects an event belonging to a specific category, and outputs a detection result, a display controller that displays, together with a video of the video data, a category setting screen for setting a category of an event included in the video, and a learning data accumulator that accumulates, as learning data together with the video data, category information set in accordance with an operation by an operator to the category setting screen. The video analyzer performs learning processing by using the learning data accumulated in the learning data accumulator.