Video Sequence Coding with Stationary and Nonstationary Feature Representation
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Solution Overview
Problem
Existing methods for coding video image sequences lack an efficient way to facilitate machine evaluation, particularly in applications like vehicle guidance where dynamic behavior analysis is crucial.
Innovation Solution
A method is developed to code a predefined time sequence of video images into a representation comprising stationary and nonstationary features, using a parameterized function trained on the video sequence. This representation is evaluable by machine for various downstream tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a parameterized function with trainable parameters is used to map video sequences to representations, then the representation quality for machine evaluation is improved, but the training complexity and computational resources required increase
Solution Approach 1:
The system performs self-monitored training where the model automatically optimizes its own parameters θ using a consistency condition based on its own predictions. The model divides a long video extract into short extracts, generates representations for both, and trains itself by minimizing the inconsistency between these representations, eliminating the need for external ground truth labels or manual intervention.
Solution Approach 2:
The method performs preliminary encoding of video sequences into stationary and nonstationary feature representations before downstream machine evaluation tasks. This pre-processing step creates a standardized representation format that facilitates subsequent actions such as action recognition, video retrieval, and compilation, improving overall system efficiency.
2Measurement precision
If ground truth labels are used for training the parameterized function, then the training accuracy is improved, but the manual work and cost increase
Solution Approach 1:
The model performs self-monitored training by using its own predictions to create training signals. It generates representations for long and short video extracts, computes a consistency condition between these representations, and uses this self-generated consistency metric as the training objective, completely eliminating the need for external ground truth labels and manual annotation work.
Solution Approach 2:
The system implements a feedback loop where the model's own predictions are used to generate training signals. The consistency condition between representations of long and short extracts provides immediate feedback for parameter optimization, allowing the model to iteratively improve its own performance without external intervention.
3Productivity
If training is performed on one video sequence, then the training efficiency is improved, but the transferability to other video sequences decreases
Solution Approach 1:
The model learns universal representation patterns that are applicable across different video sequences. By training on the consistency condition between long and short extracts from the same sequence, the model discovers generalizable features and relationships that transfer to unseen video data, enabling the same trained model to effectively process diverse video content for various downstream tasks.
Data Source
AI summary
A method for coding a predefined time sequence of video images in a representation which is evaluable by machine made up of stationary features and nonstationary features. In the method: at least one function parameterized using trainable parameters is provided, which maps sequences of video images on representations; from the sequence of video images, N adjoining, nonoverlapping short extracts and one long extract, which contains all N short extracts are selected; using the parameterized function, a representation of the long extract and multiple representations of the short extracts are ascertained; the parameterized function is assessed; the parameters of the function are optimized with the goal that the assessment of the cost function for representations ascertained in future is expected to improve; using the function parameterized by the finished optimized parameters, the predefined time sequence of video images is mapped on the sought representation.