Video Feature Extraction via Object Quality Prediction
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Solution Overview
Problem
Existing methods for extracting features from objects in videos, such as those using deep learning, face challenges in processing efficiency and accuracy, particularly when dealing with multiple objects and severe resource constraints, leading to potential extraction of unsuitable features due to considerations of size, position, and motion.
Innovation Solution
An information processing device that predicts feature quality based on positional relationships and overlap between objects, selects objects for feature extraction based on predetermined conditions, and extracts features from those objects, optimizing feature extraction for matching accuracy while reducing processing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If features are extracted from all objects appearing in the image using deep learning, then matching accuracy is improved, but processing time increases and real-time processing becomes difficult under severe calculation resource constraints
Solution Approach 1:
The patent applies partial action by extracting features from only a selected subset of objects rather than all objects in the image. The selection is based on quality assessment criteria including object size, position in the image, and motion characteristics. This selective approach reduces the number of deep learning inferences required, thereby decreasing processing time while maintaining matching accuracy for the most suitable objects.
Solution Approach 2:
The patent changes the parameter of object selection by introducing quality assessment based on multiple factors (size, position, motion). Instead of uniformly processing all objects, the system dynamically adjusts which objects receive feature extraction resources based on their predicted feature quality, optimizing the balance between accuracy and processing speed.
2Productivity
If features are extracted from only selected objects based on size and past extraction frequency, then processing efficiency is improved, but the extracted features may be unsuitable for matching
Solution Approach 1:
The patent expands the selection criteria from simple size and frequency-based parameters to a comprehensive quality assessment that includes object size, position in the image (e.g., lower part for surveillance cameras), and motion characteristics. This multi-parameter approach ensures that selected objects have high-quality features suitable for matching while maintaining processing efficiency.
Solution Approach 2:
The system incorporates feedback mechanisms by considering past feature extraction frequency and using motion detection results to inform current selection decisions. This feedback loop ensures that objects with previously successful feature extractions are prioritized, while also adapting to current scene dynamics through motion analysis.
3Device complexity
If features are extracted from objects based only on position in the image, then processing is simplified, but the extracted features may be unsuitable for matching
Solution Approach 1:
The patent enhances the position-based selection by combining it with size and motion parameters. The quality assessment function integrates multiple parameters (position, size, motion) to comprehensively evaluate feature suitability. This approach maintains relative simplicity while significantly improving feature quality compared to position-only methods.
Data Source
AI summary
In order to extract a feature suitable for comparison, the information processing device according to the present invention comprises: a prediction unit which, on the basis of the positional relationship between a plurality of objects detected and tracked in an input video and on the basis of the overlap between the plurality of objects, predicts the qualities of features extracted from the objects; a selection unit which selects, from among the plurality of objects, only those objects or that object for which the qualities of features predicted by the prediction unit satisfy a prescribed condition; and a feature extraction unit which extracts features from the objects or the object selected by the selection unit.