GPU Player Orientation Estimation via Parallel Neural Networks
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Solution Overview
Problem
Current graphics processing systems face challenges in efficiently determining the frontal body orientation of players in complex scenes, particularly in real-time applications such as video games and virtual reality, due to the complexity of processing large amounts of data and synchronizing thread execution in parallel processing architectures.
Innovation Solution
A graphics processing unit (GPU) is integrated with a parallel processing system that utilizes a machine learning model to estimate the 2D and 3D orientation of players by analyzing frames from multiple cameras, employing a SIMT architecture to maximize parallel processing efficiency and a deep neural network for image recognition and tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If parallel processing with SIMT architecture is used to process large amounts of graphics data, then processing speed and throughput are improved, but thread synchronization complexity and computational overhead increase
Solution Approach 1:
The patent segments the complex task of determining frontal body orientation into multiple independent processing stages: detecting players, determining 3D positions, estimating 2D orientations, and calculating 3D orientations. Each stage can be processed by different thread groups in parallel, reducing synchronization requirements while maintaining high throughput.
Solution Approach 2:
The patent performs preliminary actions by pre-processing graphics data to identify player positions and orientations before the main computation. Training data is pre-generated with ground truth labels, and the machine learning model is pre-trained, so that during real-time processing, only inference is needed, significantly reducing computational overhead and synchronization complexity.
2Measurement precision
If machine learning models are integrated into graphics processing to determine player orientation, then measurement precision is improved, but computational overhead and processing time increase
Solution Approach 1:
The patent replaces traditional mechanical or rule-based orientation detection methods with machine learning models that can infer player orientation from graphics data. The neural network processes visual features to estimate 2D and 3D orientations with high precision, substituting complex geometric calculations with learned patterns that run efficiently on GPU hardware.
Solution Approach 2:
The patent changes the parameters being processed by the machine learning model from raw pixel data to pre-extracted features such as player positions, bounding boxes, and visual descriptors. This parameter transformation reduces the input dimensionality and allows the model to focus on relevant features, improving both accuracy and processing speed.
3Measurement precision
If deep neural networks are used for image recognition and tracking, then detection accuracy is improved, but device complexity and energy consumption increase
Solution Approach 1:
The patent segments the deep neural network into specialized components: a player detection network, a 2D orientation estimation network, and a 3D orientation estimation network. Each network is optimized for its specific task and can be processed independently, reducing the complexity of any single model while maintaining overall detection accuracy through coordinated operation.
Data Source
AI summary
Embodiments are generally directed to methods and apparatuses for determining a frontal body orientation. An embodiment of a method for determining a three-dimensional (3D) orientation of frontal body of a player comprises: detecting each of a plurality of players in each of a plurality of frames captured by a plurality of cameras; for each of the plurality of cameras, tracking each of the plurality of players between continuous frames captured by the camera; and associating the plurality of frames captured by the plurality of cameras to generate the 3D orientation of each of the plurality of players.