Digital Scene Generator for Synthetic Crowd Data
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Solution Overview
Problem
Current methods for crowd estimation in computer vision and robotics face challenges due to the lack of high-quality, annotated datasets, with human labeling efforts being tedious, biased, and inconsistent, and privacy laws restricting the use of real-world crowd data.
Innovation Solution
The development of a digital scene generator that creates photo-realistic, scalable, and labelled synthetic crowds by reconstructing environments with aerial drone data, allowing for the generation of human models with specific characteristics and their placement in scenes, overcoming the limitations of human annotation and privacy concerns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-world crowd data is used for training, then dataset authenticity is improved, but privacy compliance deteriorates
Solution Approach 1:
The patent creates synthetic crowd data by generating virtual 3D human models and placing them in reconstructed environments. This copying approach produces realistic training data without using actual personal information from real-world footage, thus maintaining dataset authenticity while ensuring privacy compliance with regulations like GDPR.
2Measurement precision
If human annotation is used for labeling, then labeling accuracy is improved, but annotation efficiency deteriorates
Solution Approach 1:
The system generates synthetic crowd data with automatic labeling through the virtual scene generation process itself. The 3D human models are created with inherent geometric and semantic information that can be directly extracted as labels, eliminating the need for separate manual annotation steps while maintaining high labeling accuracy.
3Quantity of substance
If more crowd data is collected, then dataset quantity is improved, but annotation complexity deteriorates
Solution Approach 1:
By generating synthetic data through virtual scene construction, the system can produce unlimited quantities of diverse crowd scenarios automatically. The complexity of annotation is avoided because the synthetic data generation process inherently creates labeled data through the 3D model parameters and scene configuration, rather than requiring post-processing annotation of real images.
Data Source
AI summary
Methods, systems, articles of manufacture and apparatus to generate digital scenes are disclosed. An example apparatus to generate labelled models includes a map builder to generate a three-dimensional (3D) model of an input image, a grouping classifier to identify a first zone of the 3D model corresponding to a first type of grouping classification, a human model builder to generate a quantity of placeholder human models corresponding to the first zone, a coordinate engine to assign the quantity of placeholder human models to respective coordinate locations of the first zone, the respective coordinate locations assigned based on the first type of grouping classification, a model characteristics modifier to assign characteristics associated with an aspect type to respective ones of the quantity of placeholder human models, and an annotation manager to associate the assigned characteristics as label data for respective ones of the quantity of placeholder human models.