Video Temporal Action Proposal Generation Using Learnable Segments
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Solution Overview
Problem
Existing methods for video temporal action proposal generation are inefficient and computationally costly, particularly as video length increases, and they often produce redundant proposals requiring post-processing.
Innovation Solution
A system comprising a feature extraction module, a feature processing module based on a Transformer model with an encoder and decoder, and a prediction module, which extracts video features, processes them to capture global context, and generates temporal action proposals with improved accuracy and reduced computational burden.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methods are used for video temporal action proposal generation, then comprehensive video analysis can be achieved, but computational cost increases significantly and processing efficiency decreases
Solution Approach 1:
The video is divided into multiple segments or clips, and action proposal generation is performed independently on each segment. This segmentation approach reduces the computational complexity from processing the entire video at once to processing smaller individual segments, thereby improving processing efficiency while maintaining comprehensive video analysis coverage
Solution Approach 2:
The system performs preliminary feature extraction and segment identification before conducting detailed action proposal generation. By pre-processing the video to extract relevant features and identify potential action segments in advance, the system reduces the computational burden during the main proposal generation phase, achieving both efficiency and comprehensiveness
2Measurement precision
If more video features are extracted to improve proposal quality, then detection accuracy increases, but computational burden increases
Solution Approach 1:
The system extracts only the most relevant and discriminative video features for action proposal generation, rather than processing all possible video features. By selectively extracting key features such as temporal boundaries, motion patterns, and semantic information, the system maintains high proposal quality while reducing computational burden
Solution Approach 2:
Different feature extraction strategies are applied to different parts of the video based on local characteristics. For example, regions with high action probability receive more detailed feature analysis, while low-probability regions use simplified feature extraction, optimizing the balance between proposal quality and computational efficiency
3Reliability
If traditional proposal generation methods are used, then all possible action segments can be identified, but redundant proposals are generated requiring post-processing
Solution Approach 1:
The system dynamically adjusts the proposal generation process based on confidence scores and segment characteristics. High-confidence proposals are generated with detailed analysis, while low-confidence regions use simplified generation, reducing redundant proposals at the source and minimizing post-processing requirements while maintaining comprehensive detection coverage
Data Source
AI summary
A system and method for video temporal action proposal generation are provided. It processes video features extracted from the input video through an encoder to obtain video encoding features with global information, extracts corresponding interest segment features from the video encoding features using pre-trained proposal segments, and provides them to the decoder. The decoder generates segment features based on the interest segment features corresponding to each proposal segment and the pre-trained proposal features. These are then provided to the prediction module, generating temporal action proposal results based on the decoder's segment features. The solution in embodiments of the present invention can effectively capture global context information of the video, obtaining video encoding features with stronger representational capabilities; By introducing the several learnable proposal segments to extract the corresponding position-based feature sequences from the video encoding features, the training convergence speed is enhanced, and computational burden is reduced.