Automated Training Data Generation for Object Identification Models
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Solution Overview
Problem
The time-consuming and labor-intensive process of data labeling for training object identification models based on machine learning, which is essential for accurate object tracking in images, is costly and inefficient.
Innovation Solution
A method and system for automatically generating labeled training data by detecting and tracking moving objects in image sequences using a tracker, where the track information is used as a label, significantly reducing human effort and time required for data annotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data labeling is used to train object identification models, then labeling accuracy can be maintained, but time consumption and cost increase significantly
Solution Approach 1:
The system uses automated tracking algorithms to perform labeling themselves without human intervention. The tracker automatically generates labels by detecting and tracking objects in video sequences, allowing the system to serve itself rather than requiring manual human annotation for each data point.
Solution Approach 2:
The patent replaces the mechanical manual labeling process with an automated computational tracking system. Instead of humans manually annotating data, the system uses algorithms (such as ByteTrack) to automatically detect, track, and label objects, substituting human labor with automated mechanical-like processes.
2Measurement precision
If manual data labeling is used to train object identification models, then labeling quality can be ensured, but labor intensity and cost increase significantly
Solution Approach 1:
The automated tracking system performs labeling autonomously, eliminating the need for human annotators. The system processes video sequences independently, generating labels through computational algorithms rather than requiring human labor, thereby dramatically improving productivity while maintaining quality through algorithmic consistency.
Solution Approach 2:
The system changes the fundamental parameter of labeling from human-based to algorithm-based. By transforming the labeling process from manual annotation to automated tracking, the system achieves both high productivity and consistent quality through reproducible computational processes rather than variable human performance.
3Reliability
If sufficient labeled training data is collected through manual labeling, then model training accuracy improves, but time and cost consumption increase
Solution Approach 1:
The patent replaces manual data collection and labeling with automated tracking-based data generation. The system automatically processes video sequences, detects objects, generates tracks, and creates labeled data through computational algorithms, thereby collecting sufficient training data much faster than manual methods without sacrificing the reliability needed for accurate model training.
4Productivity
If automated tracking is used to generate labels, then time and cost for data labeling are reduced, but labeling complexity may increase
Solution Approach 1:
The patent introduces tracking algorithms (such as ByteTrack) as intermediary components that bridge the gap between raw video data and final labeled training data. These intermediaries automatically perform the complex tasks of object detection, tracking, and label generation, managing the complexity internally while presenting a simple interface for data collection.
Data Source
AI summary
A training data generation method generate labeled training data used for training an object identification model that is based on machine learning. The training data generation method includes: (A) detecting a moving object in a sequence of images; (B) tracking a same moving object in the sequence of images by using a tracker, to automatically obtain a track that is information representing a time series of the same moving object in the sequence of images; and (C) generating the labeled training data by giving the track as a label to the sequence of images.