Video Bounding Box Prelabeling for Faster, More Accurate Annotation
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Solution Overview
Problem
Manual labeling of data for machine learning is time-consuming, labor-intensive, and produces inconsistent and inaccurate labels, affecting the performance of machine learning models.
Innovation Solution
A technique for prelabeling bounding boxes in video frames using a machine learning model, such as Faster R-CNN, to generate refined estimates which are then refined further and output as initial representations for user adjustment, reducing the need for extensive user input and improving label accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is used, then users can provide detailed and accurate labels, but the process is time-consuming and labor-intensive
Solution Approach 1:
The system performs preliminary action by automatically generating initial bounding box labels using a machine learning model before user annotation. The model processes video frames and produces pre-labeled bounding boxes that serve as starting points, reducing the time users need to spend on manual labeling while maintaining accuracy through user verification and refinement.
2Reliability
If manual labeling is used, then users can control label quality, but the process is labor-intensive and produces inconsistent labels
Solution Approach 1:
The system implements feedback mechanisms where users receive automated pre-labeled bounding boxes, verify their accuracy, and provide corrections. The system uses this feedback to improve subsequent labeling operations and can track user performance to adjust labeling strategies, thereby improving label consistency while reducing the complexity of manual review processes.
3Productivity
If automated labeling is used, then labeling speed increases, but label accuracy decreases due to noisy and inaccurate labels
Solution Approach 1:
The system performs preliminary action by training a machine learning model on high-quality labeled data to generate accurate pre-labeled bounding boxes. This preliminary training enables the automated system to produce high-accuracy labels that require minimal user correction, thereby achieving both high productivity and high measurement precision.
Solution Approach 2:
The system implements feedback loops where user corrections to automated labels are fed back into the training process. This continuous feedback mechanism allows the model to learn from errors and improve its labeling accuracy over time, enabling automated labeling to achieve both speed and precision.
4Measurement precision
If extensive user input is required for labeling, then label accuracy can be maintained, but processing time and resource consumption increase
Solution Approach 1:
The system performs preliminary action by generating high-quality initial bounding box labels automatically before user review. This preliminary labeling reduces the amount of user input required, as users only need to verify and make minor corrections rather than creating labels from scratch, thereby reducing processing time while maintaining accuracy.
Solution Approach 2:
The system changes the parameter of labeling approach by transitioning from complete manual labeling to a hybrid model where automated labeling provides initial results that require minimal user verification. This parameter change in the labeling process reduces both processing time and resource consumption while maintaining label accuracy through the combination of automated generation and user validation.
Data Source
AI summary
One embodiment of the present invention sets forth a technique for performing a labeling task. The technique includes determining one or more region proposals, wherein each region proposal included in the one or more region proposals includes estimates of one or more bounding boxes surrounding one or more objects in a plurality of video frames. The technique also includes performing one or more operations that execute a refinement stage of a machine learning model to produce one or more refined estimates of the one or more bounding boxes included in the one or more region proposals. The technique further includes outputting the one or more refined estimates as initial representations of the one more bounding boxes for subsequent annotation of the one or more bounding boxes by one or more users.