Surveillance Video Event Learning With Operator Feedback
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Solution Overview
Problem
Existing behavior recognition systems in surveillance cameras lack the ability for real-time discriminator learning, leading to a decrease in analytical accuracy during operation due to the absence of operator intervention and support.
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 data for learning processing, thereby enhancing the system's analytical accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If behavior recognition is performed by machine learning with predetermined behavior characterization, then the system can operate automatically without prior learning, but the analytical accuracy cannot be improved during actual operation due to lack of operator intervention
Solution Approach 1:
The system implements feedback by displaying detection results to operators through a display unit and accepting correction inputs. Operators can review detected events and categories, then provide corrections that are fed back into the learning data accumulator. This closed-loop feedback mechanism enables continuous improvement of detection accuracy while maintaining automated operation.
Solution Approach 2:
The system performs self-learning by automatically accumulating learning data from operator corrections without requiring manual reconfiguration. The learning data accumulator automatically stores corrected data, and the behavior recognition unit automatically updates its models using this accumulated data, enabling the system to self-improve over time.
2Ease of operation
If the system operates without operator intervention, then ease of operation is improved, but discriminator learning cannot be performed and analytical accuracy deteriorates
Solution Approach 1:
The system performs preliminary learning by accumulating learning data in advance during normal operation. Rather than requiring operators to manually configure the system before use, the system continuously collects and stores corrected detection data, which is then used for subsequent learning and improvement of detection accuracy.
3Measurement precision
If extensive manual setup is performed, then initial detection accuracy can be improved, but the complexity of system setup and the time required for configuration increase
Solution Approach 1:
The system eliminates complex manual setup by performing self-learning through automatic accumulation of learning data from operator corrections during normal operation. The behavior recognition unit automatically updates its detection models using this accumulated data, removing the need for extensive manual configuration while maintaining improving detection accuracy.
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.