Abstract: A vision-based vehicle occupant detection and classification system includes a camera mounted in the vehicle interior to capture low resolution images of the seating area. Image processing algorithms account for lighting, motion, and other phenomena. A spatial-feature matrix numerically describes the content of the image. This descriptor is the result of a number of digital filters being run against a set of sub-images, derived from pre-defined window regions in the original image. This spatial-feature matrix is used as an input to an expert classifier function, which classifies this image as best representing a scenario in which the seat is (i) empty, (ii) occupied by an adult, (iii) occupied by a child, (iv) occupied by a rear-facing infant seat (RFIS), (v) occupied by a front-facing infant seat (FFIS), or (vi) occupied by an undetermined object.

Abstract: An occupant presence and child seat detection system includes several sensors to assist a capacitance based occupant presence detection system in distinguishing an occupant from a child seat on a wet vehicle seat. In one configuration, capacitance on the seating surface and wetness of the seating surface are measured. The determination of the presence of an occupant is based upon the measured capacitance and the measured wetness.

Abstract: A vehicle occupant detection/classification and posture estimation system includes a camera equipped with a wide-angle (“fish eye”) lens and mounted in the vehicle headliner captures images of all vehicle seating areas. Image processing algorithms can be applied to the image to account for lighting, motion, and other phenomena. A spatial-feature vector is then generated which numerically describes the visual content of each seating area. This descriptor is the result of a number of digital filters being run against a set of sub-images, derived from pre-defined window regions in the original image. This spatial-feature vector is used as an input to an expert classifier function, which classifies each seating area as best representing a scenario in which the seat is (i) empty, (ii) occupied by an adult, (iii) occupied by a child, (iv) occupied by a rear-facing infant seat (RFIS), (v) occupied by a front-facing infant seat (FFIS), or (vi) occupied by an undetermined object.

Abstract: A vision-based vehicle occupant detection and classification system includes a camera mounted in the vehicle interior to capture low resolution images of the seating area. Image processing algorithms account for lighting, motion, and other phenomena. A spatial-feature matrix numerically describes the content of the image. This descriptor is the result of a number of digital filters being run against a set of sub-images, derived from pre-defined window regions in the original image. This spatial-feature matrix is used as an input to an expert classifier function, which classifies this image as best representing a scenario in which the seat is (i) empty, (ii) occupied by an adult, (iii) occupied by a child, (iv) occupied by a rear-facing infant seat (RFIS), (v) occupied by a front-facing infant seat (FFIS), or (vi) occupied by an undetermined object.

Abstract: A capacitance based human touch activation and switching device includes an electrode adjacent a hand contact area. The electrode is part of a capacitor and is connected to a detection device that monitors the capacitance. When a user hand is near the electrode, the capacitance increases. Based upon the change in capacitance, the device activates a switch, such as a vehicle horn, dome light or other vehicle accessory.