Surveillance Video Analysis With Operator Feedback Learning
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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, a display controller, and a learning data accumulator, which allow for real-time learning by accumulating category information set by operators through a category setting screen, enhancing the video analysis module's learning processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If machine learning is used for behavior recognition based on past observation, then the system can automatically recognize behaviors without previous knowledge, but the analytical accuracy cannot be improved during actual operation due to lack of operator intervention
Solution Approach 1:
The system implements feedback by allowing operators to review detection results and provide corrective input through the category setting screen. The detection results are fed back to operators for verification, and their corrections are accumulated as learning data to improve future detection accuracy, creating a closed-loop system that combines automatic recognition with human expertise.
Solution Approach 2:
The system enables self-service learning by automatically accumulating detection results and operator corrections as learning data. The video analyzer performs learning processing using this accumulated data without requiring manual reconfiguration, allowing the system to continuously improve its own analytical accuracy through operational experience.
2Measurement precision
If operator intervention is added to improve detection accuracy, then learning data can be accumulated for continuous improvement, but the system complexity and operational burden increase
Solution Approach 1:
The category setting screen serves as an intermediary interface between the automatic detection system and operator expertise. It presents detection results to operators in a structured format and collects their corrections systematically, mediating the interaction between automated processing and human judgment while maintaining system manageability.
Solution Approach 2:
The system automatically manages the complexity of data accumulation and learning processing. The video analyzer autonomously accumulates detection results and operator corrections as learning data, and automatically performs learning processing without requiring operators to manually manage the complexity of the learning system.
3Measurement precision
If real-time learning processing is implemented, then the video analytical accuracy improves during operation, but the processing time and computational resources increase
Solution Approach 1:
The system implements periodic learning processing rather than continuous real-time learning. The video analyzer accumulates detection results and operator corrections as learning data over time, then performs learning processing at intervals using this accumulated data. This periodic approach allows the system to improve analytical accuracy while avoiding the computational burden of continuous real-time learning.
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.