Spatio-temporal Video Feature Extraction for Motion Analysis
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Solution Overview
Problem
Human analysis of videos to extract metadata is labor-intensive and expensive, necessitating the need for automated video analysis to identify objects and actions with minimal human intervention.
Innovation Solution
The method applies multiple spatial feature functions to two video frames, compensates for background motion, and combines temporal gradient feature functions to determine spatio-temporal feature values, which represent object motion accurately even when spatial translation is ambiguous.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple spatial feature functions are applied densely to two frames with temporal gradient analysis, then measurement precision of object motion is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex task of motion analysis into multiple spatial feature functions (e.g., gradient magnitude, gradient orientation, Laplacian) that are applied densely across the image. Each function extracts specific aspects of spatial variation, and their combinations systematically capture different motion characteristics, breaking down the complex measurement task into manageable components.
Solution Approach 2:
The patent transitions from analyzing single frame images to analyzing temporal gradients between two frames. By computing the difference in spatial feature values between frames (temporal gradient), the system adds a time dimension to the analysis, enabling detection of motion patterns that cannot be captured by spatial features alone.
2Productivity
If automated video analysis is implemented to reduce human intervention, then productivity increases, but measurement precision of complex actions may deteriorate
Solution Approach 1:
The system uses multiple spatial feature functions and their temporal gradients to create a comprehensive feature set that provides feedback about different aspects of motion. The combination of gradient magnitude, orientation, and Laplacian responses creates a rich feedback mechanism that helps automated classifiers distinguish between different types of actions and objects with high precision.
Solution Approach 2:
The patent combines multiple spatial feature functions (gradient magnitude, gradient orientation, Laplacian) into a composite feature representation. This composite approach integrates different aspects of spatial variation to create a robust feature set that maintains high measurement precision for automated classification of complex video content.
3Measurement precision
If temporal gradient feature functions are applied to spatial feature values between two frames, then measurement precision of motion change is improved, but loss of time for processing increases
Solution Approach 1:
The patent applies spatial feature functions (gradient magnitude, gradient orientation, Laplacian) to each frame before computing temporal gradients. This preliminary extraction of spatial features organizes the data in advance, making the subsequent temporal gradient computation more efficient and focused on relevant motion characteristics rather than processing raw pixel data.
Data Source
AI summary
A method of determining a spatio-temporal feature value for frames of a sequence of video. A first frame and second frame from the sequence of video are received. Spatial feature values in each of the first and second frames are determined according to a plurality of spatial feature functions. For each of the spatial feature functions, a change in the spatial feature values between the first and second frames is determined. The spatio-temporal feature value is determined by combining the determined change in spatial feature values for each of the spatial feature functions.