초록 ▼

Vehicle including a compartment receivable of an object and a system for tracking the object includes at least one imaging device each arranged to receive an image of a portion of the compartment containing the object and a control unit coupled to each imaging device and which controls the imaging d

Vehicle including a compartment receivable of an object and a system for tracking the object includes at least one imaging device each arranged to receive an image of a portion of the compartment containing the object and a control unit coupled to each imaging device and which controls the imaging device to obtain a first set of images without the object and at least one second image including the object. The control unit analyzes the second image(s) in consideration of the first set of images to derive information about the object. This information may be the type, size and/or position of the object or a part thereof. The information may be used to control vehicular components which have a variable use based on the type, size or position of the object or part thereof.

대표청구항 ▼

The invention claimed is: 1. A vehicle including a compartment receivable of an object of interest and a system for tracking the object of interest in the compartment, comprising: a seat arranged in the vehicle and including a seat bottom portion on which the object of interest is situated and a se

The invention claimed is: 1. A vehicle including a compartment receivable of an object of interest and a system for tracking the object of interest in the compartment, comprising: a seat arranged in the vehicle and including a seat bottom portion on which the object of interest is situated and a seat back portion arranged behind the object of interest; an illumination source arranged to illuminate a front surface of said seat bottom portion and said seat back portion when there is no object on said seat bottom portion; at least one imaging device each arranged to receive an image of a portion of the compartment containing the object of interest, the portion of the compartment being defined in part by said seat back portion and said seat bottom portion such that when the object of interest is not situated on said seat bottom portion, said at least one imaging device receives an image including said front surface of said seat back portion and when the object of interest is situated on said seat bottom portion, said at least one imaging device receives an image including the object of interest; and a control unit coupled to said at least one imaging device and arranged to control said at least one imaging device to obtain a first set of images without the object of interest and a second image including the object of interest, said control unit being arranged to analyze the second image in consideration of the first set of images to derive information about the object of interest, whereby the derived information can be used to control a vehicular component. 2. The vehicle of claim 1, wherein said at least one imaging device comprises a plurality of imaging devices arranged at different locations to obtain different images of a common portion of the compartment. 3. The vehicle of claim 1, wherein said control unit analyzes the second image in consideration of the first set of images to derive information about the object of interest by subtracting the second image from each image in the first set of images to determine which provides a best match and then deriving the information about the object of interest based on a differential image between the second image and the determine one of the first images which provides the best match after the subtraction process. 4. The vehicle of claim 1, wherein said control unit analyzes the second image in consideration of the first set of images to derive information about the object of interest by comparing the second image to each image in the first set of images to determine the presence of static and dynamic reflections and deriving information about the object of interest by analyzing the dynamic reflections. 5. The vehicle of claim 1, wherein said control unit analyzes the second image in consideration of the first set of images to derive information about the object of interest by inputting a matrix derived from the second image into a trained pattern recognition algorithm which is trained on images of different objects in different positions in the compartment. 6. The vehicle of claim 5, wherein the trained pattern recognition algorithm is a neural network. 7. The vehicle of claim 1, wherein said control unit is arranged to derive information about occupancy or a seating state of a seat in the compartment. 8. The vehicle of claim 1, wherein when the object of interest is a human, said control unit is arranged to determine information about a location of eyes, ears, a chest or a head of the human. 9. The vehicle of claim 1, wherein when the object of interest is a human, said control unit is arranged to determine information about a location of a chest or a head of the human at a plurality of different times so that when the location of the chest or head of the human changes indicating motion of the head or chest, said control unit tracks the motion of the head or chest through analysis of images obtained at the different times by said at least one imaging device. 10. The vehicle of claim 9, wherein said control unit is arranged to analyze images to track motion of the head or chest using differential motion or a correlation technique. 11. The vehicle of claim 1, wherein said control unit is arranged to apply a feature extraction technique to images obtained by said at least one imaging device. 12. The vehicle of claim 11, wherein said feature extraction technique is arranged to divide an entire image into small rectangular blocks, calculate feature values from each block, line up the calculated feature values from individual blocks, and then normalize the feature values. 13. The vehicle of claim 11, wherein said feature extraction technique is arranged to analyze pixels of the images by obtaining a sum of edge strengths defined as the largest differences between neighboring pixels and an orientation of an edge determined by the neighboring pixel that produces the largest difference between itself and the pixel of interest. 14. A method for tracking a human in a compartment in a vehicle, comprising: arranging at least one imaging device in a position in which it receives an image of a portion of the compartment containing the human; controlling, using a processor, the at least one imaging device to obtain a first set of images without the human and a second image including the human; analyzing, using a processor, the second image in consideration of the first set of images to determine information about a location of a chest or a head of the human; once the location of the chest or head is determined, tracking motion of the head or chest using a processor through analysis of images subsequently obtained by the at least one imaging device; and optionally controlling a component in the vehicle based on the tracking of the motion of the head or chest. 15. The method of claim 14, wherein the processor that tracks the motion of the head or chest uses differential motion or a correlation technique. 16. The method of claim 14, wherein the at least one imaging device comprises a plurality of imaging devices, further comprising arranging the imaging devices at different locations to obtain different images of a common portion of the compartment. 17. The method of claim 14, wherein the analysis of the second image in consideration of the first set of images using the processor comprises applying, via the processor, a trained pattern recognition algorithm to an array derived from the second image. 18. The method of claim 14, wherein the analysis of the second image in consideration of the first set of images using the processor comprises extracting features from an array derived from the second image. 19. The method of claim 18, wherein the step of extracting features comprises dividing the array into small rectangular blocks, calculating feature values from each block, lining up the calculated feature values from individual blocks, and then normalizing the feature values. 20. The method of claim 18, wherein the step of extracting features comprises analyze pixels of the array by obtaining a sum of edge strengths defined as the largest differences between neighboring pixels and an orientation of an edge determined by the neighboring pixel that produces the largest difference between itself and the pixel of interest.