Image Recognition for Precise Area Intrusion Detection
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Solution Overview
Problem
Conventional image recognition systems using fish-eye lenses from overhead positions may erroneously determine that a person has entered a dangerous area due to the frame enclosing the person occupying a large area in the image, despite the person being outside the dangerous area.
Innovation Solution
An image recognition apparatus that sets a frame around a person in the image and identifies a region corresponding to the person, using deep learning techniques, and outputs an area intrusion notification only when there is an overlap between the identified region and a specific area, with the region being smaller than the frame and potentially enlarged to exclude hard-to-detect areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a frame enclosing a person is used for detection, then the detection coverage is improved, but the measurement precision deteriorates due to false positive intrusions
Solution Approach 1:
The patent divides the detection process into two stages: first using a frame-based approach for broad coverage, then segmenting the frame into multiple regions (e.g., head region, body region) for precise intrusion detection. This segmentation allows the system to maintain both wide detection coverage and high measurement precision by applying different detection strategies to different parts of the detected object.
2Reliability
If the frame size is enlarged to capture the entire person, then the detection reliability is improved, but the area of the detection region increases causing more false positives
Solution Approach 1:
The patent applies local quality by assigning different importance weights to different regions within the frame. Critical regions (such as the head or upper body) are given higher priority for intrusion detection, while less critical regions are given lower priority. This allows the system to maintain reliable person detection while reducing false positives by focusing attention on the most relevant areas.
3Measurement precision
If deep learning-based region identification is implemented, then the measurement precision is improved, but the operation time and computational cost increase
Solution Approach 1:
The patent implements partial action by applying deep learning-based region identification selectively rather than to all frames uniformly. The system first uses a faster frame-based detection method, and only applies the more computationally intensive deep learning region identification when necessary (e.g., when frame-based detection is ambiguous or when intrusion is suspected). This reduces overall processing time while maintaining high measurement precision when needed.
Data Source
AI summary
An image recognition apparatus includes a controller configured to acquire an image of space containing a specific area from an imaging apparatus that captures the image, set, in the acquired image, a frame enclosing a person present in the space, identify a region corresponding to the person, at least within the frame in the image, and output an area intrusion notification upon detecting overlap in the image between the identified region and the specific area.