Video Annotation ML Training via Feedback Loop
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Solution Overview
Problem
Current video surveillance systems face challenges in accurately annotating and validating video annotation data, particularly in determining the quality and relevance of object attribute data, which affects the effectiveness of event detection and processing.
Innovation Solution
A computer-implemented method is introduced to train a video annotation machine learning process by comparing first and second sets of object attribute data, with the ability to revise the machine learning process and data when they are not sufficiently similar, ensuring accurate annotation and validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated monitoring using machine learning is implemented, then productivity is improved, but measurement precision deteriorates due to difficulty in determining quality of object attribute data
Solution Approach 1:
The system implements feedback by comparing machine learning-generated object attribute data with user-provided ground truth data. When discrepancies are detected, the system requests user feedback to correct the annotations, which are then used to retrain and improve the machine learning model, creating a continuous improvement loop that maintains both automation and precision
Solution Approach 2:
The system performs preliminary validation by comparing machine learning annotations against user-provided ground truth data before finalizing the annotation process. This preliminary check allows the system to identify and correct potential errors early, ensuring high measurement precision while maintaining automated efficiency
2Reliability
If machine learning data is continuously updated with user feedback, then reliability is improved, but device complexity increases
Solution Approach 1:
The system implements self-service by automatically using user feedback to retrain and update the machine learning model without requiring manual intervention for each update. The system autonomously manages the feedback loop, data collection, model retraining, and deployment, reducing operational complexity while improving reliability
Solution Approach 2:
The system merges multiple functions into a unified machine learning pipeline: annotation generation, quality validation, feedback collection, data retraining, and model deployment are combined into an integrated system. This consolidation manages complexity by creating a cohesive workflow rather than separate discrete systems
Data Source
AI summary
A computer-implemented method to train a video annotation machine learning process is disclosed. The method may include obtaining a video and determining that a predetermined event occurs in the video. The method may include identifying a first set of object attribute data associated with the event in the video based on a machine learning process and machine learning data. The method may include receiving a second set of object attribute data of the event in the video from a user or external source. The method may also include comparing the first set of object attribute data with the second set of object attribute data. The method may include validating the quality of the first set of object attribute data when the first set of object attribute data is determined to be sufficiently similar to the second set of object attribute data. The method may include revising the machine learning process and the machine learning data when the first set of object attribute data is determined not to be sufficiently similar to the second set of object attribute data.