Dynamic Video Sampling Window for Action Recognition
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Solution Overview
Problem
Existing video sampling methods with fixed window sizes and step lengths are inadequate for accurate action recognition, as they fail to correctly cover the entire process of actions, leading to low recognition precision due to noise and redundant computation, especially for actions of varying durations.
Innovation Solution
A video sampling method that adjusts the sampling window based on action parameters, including probability and completeness, by dynamically changing its size and position, using feature extraction and recognition to determine the optimal window adjustments, thereby ensuring the entire action process is captured accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed window size is used for video sampling, then the sampling process is simple and fast, but the action recognition precision is low because the window cannot correctly cover actions of varying durations
Solution Approach 1:
The patent applies dynamics by transitioning from a fixed window size to a dynamically adjustable window size. The sampling window size is adjusted based on action parameters (probability and completeness) detected during video processing. This allows the window to adapt to actions of varying durations, correctly covering the entire action process while maintaining reasonable computational complexity through targeted adjustments rather than exhaustive search.
Solution Approach 2:
The patent changes the parameter of window size from a fixed value to a variable that can be adjusted based on detected action characteristics. By modifying the window size parameter according to action probability and completeness metrics, the system achieves higher recognition precision for actions with different durations without requiring complete redesign of the sampling mechanism.
2Measurement precision
If a fixed step length is used for sampling, then the computation is efficient, but the entire action process is not correctly covered leading to low recognition accuracy
Solution Approach 1:
The patent makes the sampling step length dynamic rather than fixed. The step length is adjusted based on action parameters detected during processing, allowing the system to take larger steps when actions are clearly identified and smaller steps when actions are ambiguous or ongoing. This dynamic adjustment ensures complete action coverage while maintaining sampling efficiency through adaptive pacing.
3Measurement precision
If the sampling window does not cover the entire action process, then the sampling is faster, but noise increases and recognition precision decreases
Solution Approach 1:
The patent implements feedback by continuously monitoring action parameters (probability and completeness) during the sampling process. Based on this feedback, the system adjusts the window size and step length in real-time. When action completeness indicates the entire action is captured, the system can stop sampling for that segment, avoiding unnecessary processing time while ensuring complete action coverage and minimizing noise inclusion.
4Measurement precision
If the sampling window is constantly adjusted to cover actions accurately, then recognition precision improves, but redundant computation increases
Solution Approach 1:
The patent applies partial action by adjusting the sampling window only when and to the extent necessary based on detected action parameters. Rather than constantly adjusting the window throughout processing, the system performs targeted adjustments when action probability and completeness metrics indicate a need for better coverage. This avoids redundant computation while maintaining sufficient precision for accurate action recognition.
Data Source
AI summary
A video sampling method, including sampling a video based on a sampling window to obtain a current sequence of sampled images; acquiring action parameters corresponding to the current sequence of sampled images; adjusting the sampling window according to the action parameters; and sampling the video based on the adjusted sampling window.