Fusing Candidate Bounding Regions for Object Detection
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Solution Overview
Problem
Conventional object recognition services face difficulties in accurately estimating and evaluating bounding boxes, leading to inefficient object detection and recognition, particularly in cases with poorly rendered backgrounds or low-quality images, resulting in frustrating user experiences.
Innovation Solution
The system employs multiple object detection algorithms to generate candidate regions, which are then reconciled using statistical methods such as averaging or median calculation, along with confidence scores and penalty functions, to determine a representative region of interest, ensuring accurate object detection and recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional object recognition services use single algorithm for bounding box estimation, then device complexity is reduced, but measurement precision of bounding boxes deteriorates
Solution Approach 1:
The patent combines multiple object detection algorithms to generate multiple candidate bounding boxes, then merges them through fusion techniques (such as non-maximum suppression or probabilistic fusion) to produce a final refined bounding box. This merging process improves measurement precision by leveraging the strengths of different algorithms while maintaining manageable system complexity through structured integration.
Solution Approach 2:
The patent generates multiple copies of bounding box estimates from different algorithms and uses these copies to evaluate quality and determine a final result. By creating multiple candidate bounding boxes and comparing them against reference bounding boxes, the system achieves higher precision without requiring a single overly complex algorithm.
2Measurement precision
If multiple object detection algorithms are used to generate candidate regions, then measurement precision of bounding boxes is improved, but device complexity increases
Solution Approach 1:
The patent segments the object detection process into distinct modules: multiple algorithms generate candidate bounding boxes, a fusion module combines these candidates, and an evaluation module assesses quality. This segmentation allows the system to use multiple algorithms for high precision while managing complexity through modular architecture, where each component has a specific function.
Solution Approach 2:
The patent introduces intermediary components such as a fusion module and evaluation module that mediate between multiple candidate bounding boxes and the final result. These intermediaries process and reconcile the outputs from multiple algorithms, enabling the system to achieve high precision while keeping the overall complexity manageable through structured intermediate processing steps.
3Measurement precision
If reference bounding box is used to evaluate candidate regions, then measurement precision is improved, but loss of time increases due to additional evaluation steps
Solution Approach 1:
The patent performs preliminary actions by pre-processing image data and pre-computing features before generating candidate bounding boxes. This preliminary preparation enables faster evaluation of candidate regions against reference bounding boxes, reducing the time penalty associated with multiple evaluation steps while maintaining high measurement precision.
Solution Approach 2:
The patent changes parameters such as confidence thresholds and evaluation criteria dynamically based on image quality and object characteristics. By adjusting these parameters, the system can optimize the balance between evaluation accuracy and processing time, achieving high precision when needed while reducing evaluation overhead in simpler cases.
Data Source
AI summary
Image data may be downloaded or otherwise obtained. An application executing on the computing device (or at least in communication with the computing device) can analyze the image data to recognize objects represented in the image data as well as determine locations or regions of the image that include the representations. Candidate regions of interest (e.g., bounding boxes) that include at least one detected object can be generated. A single region—a representative region—can be determined from the candidate regions based on user and/or application-defined criterion. The criteria may help ensure that the representative region includes statistical properties that at least satisfy threshold property levels, such as being a consistent estimator of the candidate regions. Notions of “average” or “median” regions can be applied to generate the representative region. The representative region can be associated with a confidence score, which can reflect inconsistency in the candidate regions, and therefore can be associated with a property of the candidate regions and not of the representative region alone.