Visual Beat Detection From Object Motion for Video-Audio Sync
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Solution Overview
Problem
Existing methods for identifying visual beats in user-generated video content are inadequate, failing to distinguish between ordinary movement and important motions, requiring significant effort and processing skills beyond the abilities of typical social media users.
Innovation Solution
A system and method for automatically analyzing object motion by identifying objects and body parts in video sequences, determining movement between frames, and generating a directogram to identify visual beats, which are then emphasized through visual enhancements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated tools are used to identify visual beats, then processing efficiency is improved, but the accuracy of visual beat identification deteriorates
Solution Approach 1:
The video is divided into multiple frames, and each frame is analyzed independently to detect motion vectors. This segmentation allows the system to process video data in manageable units while maintaining high accuracy through frame-by-frame analysis. The motion vectors are then aggregated to identify visual beats, combining the benefits of automation with precise detection.
Solution Approach 2:
The system introduces a new dimension of analysis by calculating motion vectors that represent the magnitude and direction of object movement between frames. This additional dimensional data (motion vector magnitude and direction) enables more accurate visual beat identification while maintaining automated processing, as the motion vectors provide a quantitative measure of movement significance.
2Measurement precision
If manual analysis is used to identify visual beats, then measurement precision is improved, but processing time and effort increase
Solution Approach 1:
The system performs self-service analysis by automatically detecting motion vectors and identifying visual beats without requiring manual intervention. The automated algorithm processes the video data independently, using motion vector calculations to identify significant moments, thereby eliminating the time-consuming manual analysis while maintaining acceptable accuracy through quantitative motion assessment.
Solution Approach 2:
The patent replaces manual mechanical analysis with an automated computational system. Instead of human eyes and brain processing the video frames, the system uses computer vision algorithms to calculate motion vectors and identify visual beats. This substitution of mechanical (manual) processing with automated computational methods dramatically reduces processing time while maintaining consistent and repeatable accuracy.
3Device complexity
If simple motion detection is used, then device complexity is reduced, but the ability to distinguish important motions deteriorates
Solution Approach 1:
The system dynamically adjusts its analysis based on the calculated motion vectors. By continuously monitoring the magnitude and direction of motion between frames, the system can adaptively identify significant motion patterns that represent visual beats. This dynamic analysis allows the system to distinguish important motions from ordinary movement through quantitative assessment of motion characteristics.
Solution Approach 2:
The patent changes the parameters of motion detection by introducing motion vector magnitude and direction as key measurement criteria. Instead of using simple presence/absence of motion, the system analyzes the quantitative parameters of motion (vector magnitude, direction changes) to distinguish important visual beats from ordinary movement. This parameter-based approach maintains system simplicity while significantly improving motion distinction capability.
Data Source
AI summary
Methods, systems, and storage media for generating visual beats are disclosed. Exemplary implementations may: receive an input video; identify one or more objects in motion within the sequence of a plurality of video images; weight an impact envelope with a change in angle for each of the one or more objects in motion in the sequence of a plurality of video images; generate a directogram based on the impact envelope; and identify moments corresponding to one or more visual beats in the sequence of a plurality of video images for emphasis based on the directogram.