AI Head Pose Estimation Using 2D-3D Sensor Fusion
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Solution Overview
Problem
Current technologies require expensive 3D cameras to recognize a person's pose, necessitating a solution to infer 3D head pose information from 2D images using more accessible and cost-effective methods.
Innovation Solution
An AI apparatus and method that utilize a 2D image sensor to acquire images, a 3D image sensor to gather pose information, and a processor to match and correct data, extracting 3D head pose information by synchronizing and correlating 2D and 3D coordinates, enabling the inference of head rotation and position without the need for expensive 3D cameras.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a 3D camera is used to recognize a person's pose, then measurement precision of head pose is improved, but device cost increases
Solution Approach 1:
The patent creates a mapping relationship between 2D image coordinates and 3D head pose coordinates, allowing the system to copy the functionality of expensive 3D pose recognition into a 2D image processing framework. By training a neural network to learn the correspondence between 2D landmarks and 3D pose parameters, the system replicates 3D camera capabilities using affordable 2D imaging equipment.
Solution Approach 2:
The patent replaces expensive, complex 3D camera systems with inexpensive 2D cameras. The 2D camera system, combined with computational algorithms, serves as a cost-effective alternative that achieves comparable pose estimation accuracy without requiring specialized expensive hardware.
2Ease of manufacture
If a 2D camera is used to infer 3D head pose information, then device cost is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent transforms the problem by changing the parameter space - instead of directly measuring 3D pose parameters with expensive sensors, the system uses 2D image parameters (landmark coordinates) and transforms them through a learned mapping function. The neural network learns optimal parameter transformations that recover 3D pose information from 2D projections, effectively changing how the measurement is performed rather than changing the hardware.
Solution Approach 2:
The patent replaces the mechanical/optical 3D sensing system with a computational approach. Instead of using multiple cameras or specialized 3D sensors to physically capture depth information, the system uses a single 2D camera combined with machine learning algorithms to computationally infer 3D pose, substituting physical measurement mechanisms with intelligent data processing.
3Measurement precision
If 3D head pose information is corrected using 2D landmark coordinates, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-training a neural network on large datasets of corresponding 2D images and 3D pose annotations. This pre-training phase learns the complex mapping relationships between 2D landmarks and 3D pose parameters, so that during actual operation, the system can directly apply the learned model without performing complex real-time optimization or calibration procedures.
Solution Approach 2:
The patent introduces an intermediary neural network model that mediates between 2D image inputs and 3D pose outputs. This intermediary learns the complex transformation relationships during training and serves as a bridge that simplifies the inference process, converting the difficult direct mapping problem into a straightforward forward propagation through a trained network.
Data Source
AI summary
Disclosed is an artificial intelligence (AI) apparatus including a two-dimensional (2D) image sensor configured to acquire a 2D image of a head of a person, a three-dimensional (3D) image sensor configured to acquire 3D head pose information of the head, and a processor configured to match the 2D image with the 3D head pose information, to extract 3D head pose information for determining a rotation direction of the head from the 3D head pose information, to extract a 2D image matched with the extracted 3D head pose information, to acquire 3D relative coordinates as a reference for correcting the 3D head pose information based on 2D coordinates of a predetermined landmark point of the extracted 2D image, and to acquire the corrected 3D head pose information of the predetermined landmark point of each 2D image by correcting the 3D head pose information based on the 3D relative coordinates.