Video Sequential Alignment Using Hierarchical Keyframe Masking
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Solution Overview
Problem
Traditional video sequential alignment methods, such as those using dynamic time warping algorithms, face challenges with quadratic time and space complexity, limiting their effectiveness for large-scale data processing.
Innovation Solution
The proposed method employs a hierarchical sequential alignment algorithm combined with keyframe matching and the Block A* algorithm to efficiently find a minimal alignment path by generating masks for a reduced search space, reducing complexity to linear time and space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional dynamic time warping algorithms (e.g., Needleman-Wunsch, Dijkstra) are used for video sequential alignment, then alignment accuracy is maintained, but time complexity and space complexity increase quadratically to O(NM), limiting applicability to large sequences
Solution Approach 1:
The patent divides the video sequences into segments using keyframe detection and grouping similar frames into clusters. This segmentation reduces the problem size from O(NM) to O(N'M') where N' and M' are the reduced dimensions after clustering, thereby lowering computational complexity while maintaining alignment accuracy through representative frame selection
Solution Approach 2:
The patent introduces keyframes as intermediary elements that represent groups of similar frames. Instead of directly aligning all frames, the algorithm first aligns keyframes and then uses these alignments to guide the alignment of intermediate frames, acting as a mediator that reduces the search space and computational burden
2Reliability
If traditional DTW algorithms are applied to large video sequences, then comprehensive alignment is achieved, but memory consumption increases quadratically, making it infeasible for large-scale data
Solution Approach 1:
The patent extracts and removes redundant information by identifying and grouping similar frames through clustering. By extracting only the essential representative frames (keyframes) and discarding redundant duplicate frames, the algorithm reduces memory requirements from storing all frame comparisons to storing only keyframe comparisons
Solution Approach 2:
The patent transforms the alignment problem from a two-dimensional matrix computation to a hierarchical multi-dimensional approach by introducing temporal clustering and keyframe layers. This dimensional transformation allows the algorithm to process large sequences by operating on compressed representations rather than the full sequence matrix
3Measurement precision
If exhaustive search methods are used to find the optimal alignment path, then alignment precision is maximized, but processing time increases quadratically, reducing productivity
Solution Approach 1:
The patent performs preliminary actions by detecting keyframes and pre-grouping similar frames into clusters before the actual alignment process. This preprocessing step establishes the alignment framework in advance, allowing the main alignment algorithm to operate on pre-organized data structures rather than raw sequences, thereby improving processing speed without sacrificing precision
Solution Approach 2:
The patent implements a dynamic programming approach with adaptive pruning that adjusts the search strategy based on local sequence characteristics. The algorithm dynamically modifies the search space by using keyframe alignments to constrain and guide the search for intermediate frame alignments, making the processing adaptive rather than exhaustive
Data Source
AI summary
A method for video sequential alignment is provided. The method includes inputting a first video sequence and a second video sequence, and extracting features of the first video sequence and the second video sequence. The method also includes generating an expanded path using a hierarchical sequential alignment algorithm and generating masks containing search space using keyframe matching. Further, the method includes calculating the lowest path cost within search space to find a minimal alignment path using a block A* algorithm and outputting a final alignment result after applying the block A* algorithm to search space.