Attention Generation for Video Action Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current attention-based models for video streams, which focus on frame-level attention, struggle with accurate action recognition in complex scenarios involving multiple persons and interactions, such as a basketball game, due to reduced recognition accuracy.
Innovation Solution
A method for attention generation in video streams that involves generating original attention for each frame, identifying interested areas, generating local attention for these areas, and combining them with original attention to produce a total attention map, allowing for more accurate focus on relevant actions by weighting and standardizing attention matrices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If frame-level attention is used in attention-based models for video streams, then the model can process video data efficiently, but action recognition accuracy deteriorates in complex scenarios involving multiple persons and interactions
Solution Approach 1:
The patent divides the video frame processing into multiple levels: frame-level attention and region-level attention. The video frame is segmented into multiple regions, and attention is computed separately for each region. This segmentation allows the model to focus on specific areas of interest within frames, improving action recognition accuracy while maintaining overall processing efficiency.
Solution Approach 2:
The patent applies local quality by computing attention weights specifically for different regions within video frames rather than uniformly across the entire frame. Each region receives customized attention processing based on its importance, allowing the model to allocate computational resources more effectively to critical areas while maintaining efficiency in less important regions.
2Measurement precision
If region-level attention is added to improve action recognition accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The model structure is segmented into distinct components: frame-level attention module and region-level attention module. This segmentation allows for modular implementation where each module has a specific function, making the overall complex system more manageable and easier to implement while achieving improved accuracy.
Solution Approach 2:
The patent adds a spatial dimension to the attention mechanism by introducing region-level attention alongside frame-level attention. This dimensional expansion allows the model to operate at multiple levels of granularity without requiring a complete redesign of the base architecture, thus managing complexity while improving performance.
Data Source
AI summary
A computer-implemented method for attention generation is provided. In this method, a plurality of image frames can be obtained from a video stream. An original attention for a first image frame of the plurality of image frames can be generated. Then, at least one interested area can be identified in the first image frame. A local attention for each of the at least one interested area can be generated. Moreover, a total attention for the first image frame can be generated based on the original attention of the first image frame and the local attention of each of the at least one interested area.