Video Clip Alignment Using DRAQ for Semantic Event Retrieval
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Solution Overview
Problem
Conventional video alignment systems struggle with accurately identifying and temporally aligning videos with similar semantic events, especially when key events are not well-defined or repetitive, leading to inefficient computing resources and poor alignment accuracy.
Innovation Solution
The Aligned Video Retrieval (AVR) system automatically retrieves semantically similar video clips from a large-scale database and temporally aligns them using a Dynamic Relative Alignment Quality (DRAQ) score, which evaluates the temporal alignment based on a ratio of optimal and random alignment costs, reducing the need for manual review and re-alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional video alignment systems are used to identify and align videos with similar semantic events, then alignment functionality is provided, but alignment accuracy deteriorates when key events are not well-defined or repetitive
Solution Approach 1:
The system changes the parameter of event representation from relying on well-defined key events to using distributed temporal embeddings that capture semantic information across multiple time points. This allows the system to handle undefined or repetitive key events by aggregating temporal patterns rather than depending on single distinctive events.
Solution Approach 2:
The system uses lightweight temporal alignment scores and semantic similarity metrics instead of complex, computationally expensive alignment algorithms. These simpler measures can be rapidly computed and discarded, enabling efficient processing of large video datasets without requiring manual intervention for each alignment case.
2Measurement precision
If conventional video alignment systems perform manual review and re-alignment, then alignment quality can be improved, but computing resources are wasted
Solution Approach 1:
The system performs preliminary temporal alignment and semantic matching before final video retrieval, pre-computing alignment scores and storing them for quick reference. This preliminary action eliminates the need for repeated manual review and re-alignment, as the pre-computed metrics can be directly used for accurate video pairing.
Solution Approach 2:
The system uses automated temporal alignment scoring and semantic similarity computation to self-evaluate and select aligned video pairs without requiring manual review. The alignment quality is determined through self-service mechanisms using the computed metrics, eliminating wasteful computing resources associated with human intervention.
3Quantity of substance
If a large-scale video database is searched for semantically similar video clips, then retrieval completeness is improved, but processing time increases
Solution Approach 1:
The system segments the video database processing into two stages: first, rapid retrieval of candidate videos based on semantic similarity using pre-computed embeddings; second, detailed temporal alignment scoring only for the retrieved candidates. This segmentation reduces processing time by avoiding full database analysis while maintaining retrieval completeness through the two-stage approach.
Solution Approach 2:
The system performs partial alignment scoring on a subset of candidate videos rather than exhaustive analysis of all database entries. By computing temporal alignment metrics only for semantically similar candidates retrieved in the first stage, the system achieves sufficient alignment quality without the time cost of processing the entire large-scale database.
Data Source
AI summary
Embodiments are disclosed for retrieving videos for a semantic and temporal alignment between a pair of video clips. The method may include receiving a query video clip. The method may further include determining alignment ratios between the query video clip and one or more candidate video clips. The method may further include identifying an alignable video clip from the one or more candidate video clips based on the alignment ratios. The method may further include aligning the alignable video clip with the query video clip.