Object Detection Using Part-Based Layout Consistency
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Solution Overview
Problem
Current object detection and recognition technologies face challenges in accurately detecting and recognizing objects, especially when partially occluded or deformed, and in cluttered images with multiple objects of different classes, due to insufficient information and complexity.
Innovation Solution
The approach involves learning densely represented image parts with unique labels during a training phase, using a conditional random field to enforce layout consistency, and employing a classifier to estimate belief distributions for object detection and recognition, even in partially occluded scenarios, by exploiting local and long-range spatial interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional object detection methods are used, then detection speed is maintained, but detection accuracy deteriorates for partially occluded objects
Solution Approach 1:
The patent divides the object detection task into multiple stages: candidate generation, verification, and refinement. The detection process is segmented into processing different object classes separately and handling occluded vs. non-occluded regions differently, which improves accuracy without requiring complete system redesign
Solution Approach 2:
The patent performs preliminary actions by generating candidate object regions and verifying them against learned object models before final detection. This preliminary verification step allows the system to handle occluded objects more accurately by pre-identifying potential detection targets and filtering them through multiple validation stages
2Reliability
If simple detection methods are used, then computational cost is reduced, but ability to handle occlusion deteriorates
Solution Approach 1:
The patent applies different processing strategies to different regions of the image based on local characteristics. Non-occluded regions are processed using standard detection methods, while occluded regions receive specialized handling through candidate verification and refinement, optimizing computational resource allocation
Solution Approach 2:
The patent performs verification and refinement actions on candidate regions that may be excessive for non-occluded objects but necessary for occluded ones. This selective application of computational resources based on occlusion detection allows reliable handling of difficult cases without uniformly increasing computational cost for all detections
3Measurement precision
If detailed image processing is used, then recognition accuracy improves, but processing time increases
Solution Approach 1:
The patent segments the image processing into candidate generation and verification stages, allowing detailed processing to be applied selectively to promising candidate regions rather than the entire image, thus maintaining accuracy while reducing overall processing time
Solution Approach 2:
Detailed image processing and verification are applied partially only to candidate regions that pass initial filtering, rather than to all image elements. This selective detailed processing maintains recognition accuracy for critical cases while avoiding unnecessary computational overhead in low-priority areas
4Reliability
If traditional methods are used, then processing speed is maintained, but handling of cluttered images deteriorates
Solution Approach 1:
The patent segments cluttered images into multiple candidate regions and processes them independently through verification stages, allowing the system to handle complex scenes with multiple objects by dividing the problem into manageable sub-tasks that can be processed in parallel or sequence
Solution Approach 2:
The patent performs preliminary candidate generation and filtering before final detection, which in cluttered images helps eliminate false positives from background elements and other objects. This preliminary action maintains detection speed by quickly discarding non-relevant regions while preserving detailed analysis for promising candidates
Data Source
AI summary
During a training phase we learn parts of images which assist in the object detection and recognition task. A part is a densely represented area of an image of an object to which we assign a unique label. Parts contiguously cover an image of an object to give a part label map for that object. The parts do not necessarily correspond to semantic object parts. During the training phase a classifier is learnt which can be used to estimate belief distributions over parts for each image element of a test image. A conditional random field is used to force a global part labeling which is substantially layout-consistent and a part label map is inferred from this. By recognizing parts we enable object detection and recognition even for partially occluded objects, for multiple-objects of different classes in the same scene, for unstructured and structured objects and allowing for object deformation.