Self-Attention Neural Network for Video Action Recognition
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Solution Overview
Problem
Conventional technologies face challenges in accurately and efficiently recognizing human actions in surveillance videos, which is crucial for improving care quality and reducing personnel costs in healthcare and elder care settings.
Innovation Solution
The implementation of a self-attention deep neural network architecture that incorporates spatial transformer modules to analyze 3D input tensors, generating attention windows and transformation parameters to focus on key subjects and environments, and using convolutional neural networks to extract features for action classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional action recognition methods are used, then implementation is simpler, but recognition accuracy is lower
Solution Approach 1:
The network architecture is segmented into multiple specialized components: spatial transformer modules for spatial transformation, attention window generation for region selection, and convolutional neural networks for feature extraction. Each component handles a specific aspect of action recognition, improving overall accuracy while maintaining modular complexity management.
Solution Approach 2:
The patent transforms 3D input tensors through spatial transformations in multiple dimensions, generating attention windows that focus on relevant spatio-temporal regions. This dimensional transformation approach enables the network to capture complex spatio-temporal features more effectively, improving recognition accuracy.
2Measurement precision
If comprehensive video analysis is performed, then recognition accuracy improves, but computational cost increases
Solution Approach 1:
The attention window generation mechanism extracts and focuses computation on only the most relevant spatio-temporal regions of the video input, rather than processing the entire video frame uniformly. This selective extraction reduces computational cost while maintaining recognition accuracy by concentrating resources on informative regions.
Solution Approach 2:
The spatial transformer modules perform partial transformations on selected regions rather than complete transformations on entire video frames. This partial action approach reduces computational burden while still capturing sufficient features for accurate action recognition.
3Loss of time
If manual annotation is used for training, then model training is simpler, but time consumption increases
Solution Approach 1:
The spatial transformer modules automatically learn and generate attention windows and transformation parameters from the input data without requiring manual annotation or supervision. The network performs self-service by autonomously identifying relevant regions and transformations, eliminating time-consuming manual labeling while maintaining training effectiveness.
4Measurement precision
If spatial transformations are applied to all channels, then feature extraction is more comprehensive, but processing speed decreases
Solution Approach 1:
Different spatial transformer modules process different channels with specialized transformations tailored to each channel's characteristics. This local quality approach applies appropriate transformations only where needed, improving feature extraction quality while avoiding unnecessary processing that would slow down the system.
Data Source
AI summary
An artificial neural network for analyzing input data, the input data being a 3D tensor having D channels, such as D frames of a video snippet, to recognize an action therein, including: D spatial transformer modules, each generating first and second spatial transformations and corresponding first and second attention windows using only one of the D channels, and transforming first and second regions of each of the D channels corresponding to the first and second attention windows to generate first and second patch sequences; first and second CNNs, respectively processing a concatenation of the D first patch sequences and a concatenation of the D second patch sequences; and a classification network receiving a concatenation of the outputs of the first and second CNNs and the D sets of transformation parameters of the first transformation outputted by the D spatial transformer modules, to generate a predicted action class.