Multimodal Video Loop Recognition for Higher Detection Accuracy
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Solution Overview
Problem
Existing video loop recognition methods inaccurately determine duplicate video frames due to focusing solely on image features, failing to utilize the full range of video information, which includes various modalities such as video, audio, and text, leading to reduced accuracy in video loop recognition.
Innovation Solution
A video loop recognition method utilizing a target network model that incorporates multiple sequence models for video and audio modalities to determine similarity results, enabling a comprehensive comparison of video clip pairs to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional video analysis methods are used to identify looping videos, then detection capability is limited, but computational resource consumption and processing time increase significantly
Solution Approach 1:
The patent divides video processing into multiple stages: initial frame sampling, feature extraction, similarity comparison, and loop verification. By segmenting the processing pipeline and applying different strategies at each stage (e.g., skipping frames during initial analysis, using hash-based filtering), the system achieves accurate loop detection while minimizing overall processing time and computational resources.
Solution Approach 2:
The patent performs preliminary actions by pre-processing video frames to extract key features and create compressed representations before actual loop detection. It also implements preliminary filtering mechanisms that quickly eliminate non-looping videos based on basic criteria, reserving intensive computational resources only for videos that pass initial screening, thus reducing average processing time while maintaining detection precision.
2Reliability
If comprehensive video analysis is performed to ensure accurate loop detection, then detection precision improves, but computational resource consumption increases
Solution Approach 1:
The patent applies partial action by performing comprehensive analysis only on video segments that are suspicious of containing loops, rather than analyzing every frame of every video uniformly. It uses progressive verification where the depth of analysis adapts based on initial findings, applying excessive detail only where necessary to confirm loop patterns, thus maintaining high reliability while reducing overall computational resource consumption.
Solution Approach 2:
The patent introduces intermediary structures such as feature vectors, hash representations, and intermediate similarity scores that bridge raw video data and final loop detection results. These intermediaries enable efficient filtering and comparison operations, allowing the system to maintain high detection reliability through multiple verification stages while significantly reducing the computational burden compared to direct full-video analysis.
3Measurement precision
If detailed frame-by-frame analysis is used to detect loops, then detection accuracy improves, but processing speed decreases
Solution Approach 1:
The patent implements periodic action by analyzing video frames at strategically selected intervals rather than continuously. It uses periodic sampling to identify potential loop points, then applies detailed frame-by-frame analysis only at these periodic checkpoints where loops are suspected, maintaining high detection accuracy while dramatically improving processing speed compared to continuous analysis.
Solution Approach 2:
The patent performs preliminary actions by pre-identifying candidate loop regions using low-cost heuristic methods before applying accurate but computationally intensive frame-by-frame comparison. This preliminary filtering step prepares the data in advance, allowing detailed analysis to be focused only on relevant segments, thus achieving high accuracy without sacrificing overall processing speed.
Data Source
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AI summary
Embodiments of the present application disclose a video loop recognition method and apparatus, a computer device, and a storage medium. The method comprises: acquiring a target video clip pair in a video to undergo recognition, and determining a first target encoding feature and a second target encoding feature; acquiring a target network model; inputting the first target encoding feature into a first target sequence model associated with first modal information, and outputting a first target similarity result for the target video clip pair; inputting the second target encoding feature into a second target sequence model associated with second modal information, and outputting a second target similarity result for the target video clip pair; and comparing the first target similarity result with the second target similarity result, and obtaining a cyclic comparison result for the target video clip pair. By adopting the embodiments of the present application, the accuracy of video loop recognition can be improved.