Automated Video Object Labeling Through Virtual Scene Correlation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The development of large, well-labeled datasets for machine learning models is expensive due to the significant human and computational resources required for labeling and verification, creating barriers for smaller companies and research teams.
Innovation Solution
A labeling framework that automates the process of labeling objects in visual data by initially applying a ground truth label and then automatically applying it multiple times across various images or video frames, using methods such as LiDAR scans and SLAM algorithms to generate labeled training datasets without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human experts manually label objects in visual data to ensure high accuracy, then labeling precision is improved, but time consumption and labor costs increase significantly
Solution Approach 1:
The system performs preliminary automated labeling using AI models before human verification, pre-populating truth data with predicted object locations and labels. This preliminary action reduces the subsequent manual work required while maintaining high accuracy through human review of the pre-labeled data.
Solution Approach 2:
An automated AI-based labeling system serves as an intermediary between raw visual data and final labeled datasets. This intermediary performs initial labeling operations, reducing the direct human labor required while maintaining quality through subsequent verification steps.
2Measurement precision
If human experts manually verify and label each object to ensure high quality truth data, then labeling quality is improved, but labor costs and operational complexity increase
Solution Approach 1:
The system implements self-service automated labeling capabilities where AI models perform the initial labeling task without requiring extensive human intervention. The automated system serves itself by generating preliminary labels that are then verified or corrected by human operators only when necessary, reducing overall operational complexity.
Solution Approach 2:
Instead of requiring full manual verification of every labeled object, the system applies partial human review only to cases where the automated model is uncertain or where verification is most critical. This partial action approach maintains quality while reducing operational burden.
3Reliability
If large volumes of visual data are processed to train machine learning models, then model performance is improved, but computational costs and processing time increase
Solution Approach 1:
The system performs preliminary automated labeling of large volumes of visual data using trained AI models, creating labeled datasets efficiently. This preliminary action enables subsequent model training on extensive datasets without proportionally increasing human labor costs, as the automated system can process large volumes rapidly.
Solution Approach 2:
The system uses synthetic data generation and augmentation techniques to create copies and variations of labeled datasets. This allows training on larger effective dataset sizes without proportionally increasing the cost of creating unique labeled examples, as synthetic copies can be generated computationally once the initial labeling is done.
Data Source
AI summary
An automatic visual data labeling framework for heterogeneous data types is described. An example method can include obtaining scan data and video data that each depict a scene including an object. The scan data can be generated by a scanner. The video data can be generated by a camera. The method can also include generating a virtual representation of the scene in a virtual environment based on the scan data. The virtual representation can include a virtual representation subset corresponding to the object. The virtual environment can be associated with a virtual camera. The method can also include applying label data to the virtual representation subset to create a labeled virtual representation subset corresponding to the object. The method can also include applying the labeled virtual representation subset to the object depicted in the video data based on a correlation of the scanner, the camera, and the virtual camera.