Deep 3D Attention LSTM for Video Action Recognition
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Solution Overview
Problem
Existing video-based action recognition systems fail to effectively capture long-range temporal dependencies and identify key segments in video sequences, limiting their ability to detect complex actions in real-time monitoring applications.
Innovation Solution
A deep 3D attention Long Short-Term Memory (LSTM) system is employed, which generates feature vectors for video frames using a deep convolutional neural network and applies both within-frame and between-frame attention mechanisms to identify target actions, enabling the system to capture complex temporal dependencies and detect key segments in video sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If standard classifiers or RNNs are used for action prediction without attention mechanisms, then the system complexity is reduced, but the ability to capture long-range temporal dependencies and identify key segments deteriorates
Solution Approach 1:
The patent introduces attention mechanisms as an intermediary component between feature extraction and action prediction. The attention mechanism selectively weights different temporal segments and spatial regions, enabling the system to capture long-range dependencies without requiring complex architectural changes. This mediator allows standard classifiers and RNNs to achieve improved performance by focusing computational resources on relevant video segments.
2Loss of information
If feature tracking is performed based on displacement information from optical flow fields, then motion information is captured, but the ability to identify key segments and handle complex temporal dependencies deteriorates
Solution Approach 1:
The patent applies segmentation by dividing the video sequence into multiple temporal segments and spatial regions, then applying attention mechanisms to identify and weight key segments. This segmentation approach allows the system to process motion information from optical flow while simultaneously identifying which segments contain actionable information, resolving the contradiction between capturing motion and identifying key segments.
Solution Approach 2:
The attention mechanism implements local quality by assigning different weights to different temporal and spatial regions based on their relevance to the target action. Instead of uniformly processing all video segments, the system focuses computational resources on locally important regions, thereby improving key segment identification while maintaining motion information capture capabilities.
3Reliability
If between-frame attention is applied, then temporal dependencies are captured, but within-frame spatial dependencies and key region identification deteriorate
Solution Approach 1:
The patent merges between-frame attention and within-frame attention mechanisms into a unified attention system. The between-frame attention component captures temporal dependencies across video segments, while the within-frame attention component identifies key spatial regions. By combining these two attention mechanisms, the system simultaneously achieves both temporal dependency capture and precise key region identification without compromising either capability.
Data Source
AI summary
A method, a computer program product, and a system are provided for video based action recognition. The system includes a processor. One or more frames from one or more video sequences are received. A feature vector for each patch of the one or more frames is generated using a deep convolutional neural network. An attention factor for the feature vectors is generated based on a within-frame attention and a between-frame attention. A target action is identified using a multi-layer deep long short-term memory process applied to the attention factor, said target action representing at least one of the one or more video sequences. An operation of a processor-based machine is controlled to change a state of the processor-based machine, responsive to the at least one of the one or more video sequences including the identified target action.