Video Frame Sampling via Binary Tree Traversal
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Solution Overview
Problem
Existing video analysis methods require sequential processing of all frames, which is inefficient, especially for long videos, and can be time-consuming, impacting the speed and accuracy of analysis.
Innovation Solution
A multiple iteration technique using breadth-first traversal of a binary tree to progressively sample video frames, allowing for partial analysis that represents the full video length without processing every frame, thereby refining the fidelity of the analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sequential analysis of all frames is performed, then analysis completeness is improved, but processing time increases
Solution Approach 1:
The patent segments the video frames into multiple groups and uses a binary tree structure to organize them. Instead of processing all frames sequentially, the system divides the frame sequence into segments represented by tree nodes, allowing selective processing of only necessary segments. This segmentation enables the system to achieve satisfactory analysis results without processing every single frame, thus reducing processing time while maintaining acceptable completeness.
Solution Approach 2:
The patent applies partial action by selecting only a subset of frames for analysis based on the binary tree traversal. The system determines a sample frame order that includes only necessary frames to achieve satisfactory evaluation, rather than analyzing all frames. This partial action approach reduces the total processing workload while maintaining sufficient analysis quality, directly addressing the contradiction between completeness and processing time.
2Measurement precision
If all frames are processed sequentially, then analysis accuracy is improved, but productivity decreases
Solution Approach 1:
The patent segments the frame analysis task into a binary tree structure where each node represents a segment of frames. This segmentation allows the system to process only necessary segments based on the traversal order, achieving satisfactory analysis accuracy without processing all frames. By dividing the large task into manageable segments, the system maintains acceptable accuracy while significantly improving processing speed.
Solution Approach 2:
The patent implements partial action by determining a sample frame order that includes only the necessary frames for satisfactory evaluation. The binary tree traversal algorithm selectively identifies frames that provide sufficient information for accurate analysis, avoiding unnecessary processing of all frames. This approach maintains adequate analysis accuracy while dramatically improving productivity by reducing the total number of frames processed.
3Manufacturing precision
If multiple iterations with breadth-first traversal are used, then analysis fidelity is improved, but device complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-establishing the binary tree structure to represent the frame sequence before actual analysis begins. The tree is built with nodes representing frame segments, and the traversal order is predetermined. This preliminary structuring simplifies the analysis process during execution, as the system only needs to follow the pre-defined tree traversal rather than making complex decisions during processing. This reduces operational complexity while achieving high analysis fidelity through multiple iterations.
Solution Approach 2:
The patent segments the complex frame analysis problem into a structured binary tree representation. Each node in the tree represents a manageable segment of frames, and the breadth-first traversal provides a systematic approach to processing. This segmentation transforms the complex task of analyzing all frames into a series of simpler sub-tasks, reducing the effective complexity of the algorithm while maintaining high analysis fidelity through iterative refinement.
Data Source
AI summary
A system and/or method configured to determine a sample frame order for analyzing a video. The video may have multiple frames ordered in a sequence from a beginning to an end. A first sample frame order for analyzing the video may be determined. Determining the first sample frame order may include determining an initial frame for a first iteration, and determining secondary frames for a second iteration. Determining the initial frame and the secondary frames may be based on a function of frame position in the sequence of frames. The initial frame may be associated with a first sample position, and the secondary frames may be associated with secondary sample positions in the sample frame order. A first feature of the video may be determined based on an analysis of the frames in the video performed on the frames in the first sample frame order.