Cooking Chamber Opening Motion Detection With Reduced Sensing Area
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Solution Overview
Problem
Existing methods for detecting the direction of movement of objects, such as cooking accessories, in the cooking chamber opening of cooking appliances require a large detection area and sophisticated camera systems, making them resource-intensive and prone to errors, especially with low-contrast objects.
Innovation Solution
A method that uses a reduced detection area and assigns insertion images to object categories based on the ratio of occupied to unoccupied area, allowing for the determination of direction of movement by sequencing these categories, which can be done with a variety of sensors including 3D cameras or simpler technologies like laser triangulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large detection area is used to track object movement, then the reliability of direction detection is improved, but the device complexity and processing resources required increase
Solution Approach 1:
The patent segments the detection task by dividing the detection area into multiple zones (first detection zone, second detection zone, third detection zone) along the loading axis. Instead of analyzing the entire large detection area uniformly, the system processes each zone separately and combines the results, reducing the computational complexity while maintaining detection reliability.
Solution Approach 2:
The patent introduces a new dimension for analysis by creating one-dimensional insertion images that represent the two-dimensional detection area along the loading axis. This dimensional transformation simplifies the complex two-dimensional image processing into more manageable one-dimensional data structures, reducing processing requirements.
2Reliability
If a large detection area is monitored, then object movement tracking reliability is improved, but the processing time and resources increase
Solution Approach 1:
The patent divides the image processing task into segments by processing different detection zones separately. The controller evaluates images in the first detection zone, then the second, then the third zone in sequence, rather than processing the entire large detection area at once, thereby reducing overall processing time while maintaining tracking reliability.
Solution Approach 2:
The patent applies partial action by focusing processing efforts only on the necessary portions of the detection area at any given time. By using insertion images that represent the detection area along the loading axis and processing zones sequentially, the system performs only the minimum necessary processing to achieve reliable tracking.
3Device complexity
If simpler sensor technologies are used, then the device complexity is reduced, but the ability to calculate optical flow and detect movement is lost
Solution Approach 1:
The patent replaces the complex optical flow calculation mechanism (required by 3D cameras) with a simpler image comparison mechanism. By using insertion images that capture the detection area along the loading axis and comparing these simplified representations, the system achieves movement detection without requiring sophisticated optical flow algorithms.
Solution Approach 2:
The patent introduces insertion images as an intermediary representation between the raw sensor data and the movement detection process. These insertion images, which represent the detection area along the loading axis, serve as a simplified intermediate form that can be processed by simpler sensors and algorithms while still enabling accurate movement detection.
4Device complexity
If the detection area is reduced, then the device complexity and cost are reduced, but the ability to reliably detect object direction is compromised
Solution Approach 1:
The patent compensates for the reduced detection area by changing the dimension of analysis. Instead of relying on a large two-dimensional detection area, the system uses one-dimensional insertion images that represent the detection area along the loading axis. This dimensional change allows reliable direction detection with a smaller, simpler detection area.
Solution Approach 2:
The patent applies local quality by focusing the detection system's capabilities on the specific dimension most relevant for direction detection (along the loading axis). By optimizing the detection area and processing along this critical axis rather than uniformly across the entire detection area, the system achieves reliable direction detection with reduced overall complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces the complexity and cost of image processing, enabling reliable detection of object movement with a smaller detection area and lower sensor requirements, improving accuracy for both high and low-contrast objects.
Implementation Method 1
an insertion sensor, in particular a 3D camera system (e.g. time-of-flight camera, triangulation 3D camera)
Implementation Method 2
simpler sensor technologies (e.g. laser triangulation and time-of-flight laser scanners)
Data Source
Figure 1~2
Figure 3a~4c
Figure 5a~6c
AI summary
Method for detecting the direction of movement (R) of a predetermined object (20), in particular a cooking accessory, in the region of a cooking chamber opening (22) of a cooking appliance (10), with the following steps: a) continuous or repeated, in particular regularly repeated, creation of insert images (32) a detection area (28) by means of an insertion sensor (26), b) assigning the inserted images (32) to a respective object category (F, G, A) and noting the assigned object category (F, G, A) in a memory (19') , at least if the object category (F, G, A) differs from the object category (F, G, A) last noted in the memory (19'), c) determining the direction of movement (R) of the object (20) based on the sequence of the object categories (F, G, A) recorded in the memory (19'), the object categories (F, G, A) being recorded in the memory (19') in chronological order.