Forecasting Multiple Poses Using Recurrent Neural Network
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Solution Overview
Problem
Current computer vision systems that predict human actions based on visual inputs can only forecast a single subsequent frame, which is inadequate for accurately determining the intent of a subject, potentially leading to frustration or safety issues in interactive systems like robots.
Innovation Solution
A forecasting neural network comprising an encoder, a recurrent neural network (RNN), and a decoder is used to extract features from an input image, modify them based on memory information, and produce a sequence of forecasted poses, enabling the prediction of multiple future poses and motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single-frame prediction system is used, then the system complexity is low, but the accuracy of determining subject intent is insufficient
Solution Approach 1:
The prediction task is segmented into multiple independent frame predictions (current frame, next frame, frame after next). Each frame is predicted separately using the same neural network architecture, allowing the system to capture temporal evolution of poses without requiring a single complex multi-step prediction model.
Solution Approach 2:
The system performs preliminary feature extraction from the input image once, then reuses these extracted features across multiple prediction steps. The encoder processes the input image to generate pose features that are then fed into the prediction network for generating multiple future poses, avoiding redundant processing.
2Adaptability or versatility
If multiple future poses are forecasted, then the capability to understand human motion is enhanced, but the computational complexity increases
Solution Approach 1:
A single neural network architecture serves multiple functions: it extracts pose features from images, predicts current pose, and generates multiple future poses sequentially. The same network parameters are reused across all prediction steps, making the system versatile without proportionally increasing complexity.
Solution Approach 2:
The prediction process is continuous and iterative, where each predicted pose becomes the basis for the next prediction. The network continuously generates poses in sequence, maintaining temporal coherence and providing smooth motion prediction rather than isolated discrete predictions.
Data Source
AI summary
A forecasting neural network receives data and extracts features from the data. A recurrent neural network included in the forecasting neural network provides forecasted features based on the extracted features. In an embodiment, the forecasting neural network receives an image, and features of the image are extracted. The recurrent neural network forecasts features based on the extracted features, and pose is forecasted based on the forecasted features. Additionally or alternatively, additional poses are forecasted based on additional forecasted features.