Video Feature Extraction via Frame Weighting
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Solution Overview
Problem
Conventional video feature extraction methods are limited to extracting local features, failing to provide a complete and accurate description of video sequences, resulting in poor robustness.
Innovation Solution
A method involving a device with a processor and storage that performs video frame feature extraction, calculates feature weights based on similarity matrices, and weights video frame features to obtain comprehensive video features, utilizing deep neural networks for feature extraction and weighting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional video feature extraction methods are used, then the extraction process is simple, but only local features can be extracted and the description of video sequences is incomplete
Solution Approach 1:
The patent segments the video sequence into multiple video frames and extracts features from each frame independently using a deep neural network. This segmentation allows comprehensive capture of global feature information while maintaining manageable processing complexity through modular frame-level extraction followed by aggregation.
Solution Approach 2:
The patent merges the feature vectors extracted from multiple individual video frames into a comprehensive video sequence feature representation. By combining features from all frames with calculated weights, the method achieves complete feature description while distributing the computational complexity across multiple simpler frame-level operations.
2Reliability
If conventional video feature extraction methods are used, then the computational resources are saved, but the robustness of video feature extraction is poor
Solution Approach 1:
The patent introduces a feedback mechanism by calculating the similarity between feature vectors of different video frames and using this similarity information to determine weights for each frame's contribution to the final feature representation. This feedback loop ensures that frames with more discriminative power receive higher weights, improving robustness while avoiding uniform processing of all frames.
Solution Approach 2:
The patent dynamically changes the weight parameters assigned to each video frame based on the similarity of their feature vectors. Frames with unique and discriminative features receive higher weights, while similar frames receive lower weights. This parameter adaptation improves extraction robustness by emphasizing informative frames without uniformly increasing computational resources.
3Measurement precision
If feature weights are calculated based on similarity matrices, then the accuracy of feature representation is improved, but the calculation complexity increases
Solution Approach 1:
The patent calculates similarity matrices and weights for video frames selectively based on their contribution to the overall feature representation. Rather than performing exhaustive calculations for all possible frame combinations, the method focuses on calculating weights for frames that provide unique information, achieving accurate feature representation with reduced calculation complexity.
Data Source
AI summary
A method for extracting video features may include: obtaining a target video sequence that comprises a number of video frames; performing video frame feature extraction on the target video sequence to obtain video frame features of each of the video frames; performing feature weight calculation on each of the video frame features to obtain the feature weight of each of the video frame features; wherein the feature weight of each of the video frame features is determined by the video frame features of all of the video frames in the target video sequence; and performing feature weighting on each of the video frame features according to the feature weight of each of the video frame features to obtain video features of the target video sequence.