Hierarchical Object Analysis for Multi-Resolution Detection
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Solution Overview
Problem
Existing object analysis methods in images and videos struggle with efficiency and accuracy, particularly in handling multiple resolutions and compression levels, and there is a need for improved real-time object detection and recognition systems.
Innovation Solution
A hierarchical system is employed, utilizing a tiered hierarchy of representations with different levels of quality (LoQs) for encoding and decoding signals, where object analysis is performed at multiple LoQs, leveraging convolutional neural networks (CNNs) and other neural networks to enhance detection and recognition accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If object analysis is performed on high-resolution images, then detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies segmentation by dividing the image processing task into multiple resolution levels. Low-resolution analysis is performed first to identify candidate regions, followed by high-resolution analysis only on those candidate regions. This segmented approach maintains detection accuracy while reducing overall processing time by avoiding full high-resolution analysis of the entire image.
Solution Approach 2:
The patent implements preliminary action by performing low-resolution object analysis before high-resolution analysis. The low-resolution pass serves as a preliminary filtering step that identifies promising candidate regions, allowing the system to prepare for and focus subsequent high-resolution analysis only where needed, thereby reducing total processing time while maintaining accuracy.
2Measurement precision
If multiple resolution levels are analyzed, then object detection accuracy is improved, but device complexity increases
Solution Approach 1:
The patent manages device complexity through segmentation of the analysis pipeline into distinct resolution levels (low-resolution and high-resolution passes). Each level uses appropriately scaled neural network models, reducing the computational burden at each stage while maintaining overall detection accuracy. This segmented architecture makes the complex multi-resolution system more manageable and efficient.
Solution Approach 2:
The patent resolves complexity by adding a resolution dimension to the analysis process. Instead of using a single complex high-resolution model, the system operates across multiple resolution dimensions, using low-resolution models for initial screening and high-resolution models for detailed analysis. This dimensional approach simplifies individual model complexity while achieving superior overall performance.
3Productivity
If low-resolution analysis is performed first, then processing efficiency is improved, but detection accuracy may be reduced
Solution Approach 1:
The patent reconciles this contradiction by using low-resolution analysis as a preliminary filtering action rather than a final detection step. The low-resolution pass efficiently identifies candidate regions with acceptable accuracy, and subsequent high-resolution analysis refines the detection for those specific candidates. This two-stage preliminary-action approach maintains both processing efficiency and final detection accuracy.
Solution Approach 2:
The patent resolves the accuracy-efficiency tradeoff by operating in multiple resolution dimensions. The low-resolution dimension provides efficient initial screening, while the high-resolution dimension provides accurate final detection. By transitioning between these dimensions strategically, the system achieves both processing efficiency and detection accuracy that neither single-resolution approach could achieve alone.
Data Source
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AI summary
A method comprising performing object detection within a set of representations of a hierarchically-structured signal, the set of representations comprising at least a first representation of the signal at a first level of quality and a second representation of the signal at a second, higher level of quality.