Patent Description:
Imaging devices, such as digital cameras, are used in automotive applications to detect seat occupancies in a cabin of a vehicle. However, they tend to consume much computing power and to be unreliable.

<CIT> discloses a seated state detection device. <CIT> discloses a front seat vehicle occupancy detection via seat pattern recognition.

Accordingly, there is a need to provide an improved computer implemented method, computer system and non-transitory computer readable medium for detecting an occupancy of a seat.

The present disclosure provides a computer implemented method, a computer system and a non-transitory computer readable medium according to the independent claims. Embodiments are given in the subclaims, the description and the drawings.

In one aspect, the present disclosure is directed at a computer implemented method for detecting an occupancy of a seat within a vehicle cabin.

The method comprises to capture an image of the vehicle cabin, to identify a set of characteristics associated with a seat in the captured image, to determine a region associated with the identified seat based on the set of characteristics, wherein the region is configured to cover at least a portion of the seat, and to determine a seat occupancy status of identified seat by processing information obtained from the corresponding region.

In the first step according to the method, an image is captured, by using an imaging device. The imaging device may, for example, be a camera that may be mounted in or on the roof lining, in or on the rear-view mirror or in or on the dashboard of a vehicle, capturing at least a part of the inside of a vehicle cabin.

The vehicle cabin, which may also be identified as a passenger compartment or a passenger cabin, comprises at least one seat, which may be identified as the first seat or in particular the driver's seat, and typically comprises a second, a third, a fourth and a fifth seat. In particular, the vehicle cabin comprises a driver's seat, a front-row passenger's seat and two or three back-row passenger's seats. In particular, all five seats are covered in the captured image.

In a further step according to the method, a set of characteristics associated with a seat is identified in the captured image by using a processing device, such as a microprocessor. In particular, the set of characteristics relate to parts or components of a seat in the cabin, which will be described in more detail below.

In a further step according to the method, a region associated with the identified seat is determined by the processing device based on the set of characteristics. Therein, the region is configured to cover at least a portion of the seat.

In particular, by identifying the characteristics and therefrom the region, a seat is identified.

In another step according to the method, a seat occupancy status of the identified seat is determined by using the processing device, in particular by processing information obtained from the corresponding region. An occupancy status is for example occupied or not occupied.

In particular, the identification of the set of characteristic may be used to train a machine-learning model. Therefore, the machine-learning model may be trained with several images of different vehicle cabins of different cars and/or of different camera types, in which the characteristics are identified. The machine-learning model may then be used in live application for other, in particular new type of vehicle cabins to identify the first and second characteristic.

Through this solution, a method for detecting a seat occupancy is provided, which is adaptable to other vehicles having different vehicle cabins. In particular, by identifying only some characteristic points, the detection rate is highly accurate.

According to an embodiment, the seat occupancy status is determined by comparing the information obtained from the corresponding region with reference information obtained from the corresponding region.

According to an embodiment, the reference information is obtained from at least one previously captured image. In particular, the reference information may be obtained from multiple previously captured images.

According to an embodiment, the set of characteristics comprise a first characteristic and a second characteristic defining the region associated with the seat.

Therein, the location of the first characteristic and the location of the second characteristic together define the region and the region covers a portion of the seat. In particular, the region is spanned from the location of the first characteristic to the location of the second characteristic. Typically, the region covers at least a part or the whole of the back rest area of the seat.

In particular, the first characteristic and the second characteristic are related to the first seat, which may be, as explained above, the driver's seat.

According to an embodiment, the set of characteristics further comprise a third characteristic and a fourth characteristic defining the region associated with the seat in the cabin.

In particular, the third and fourth characteristic may relate to the same, i.e. the first seat, or to a second seat. Further in particular, the four characteristics together may define one region associated with one seat.

Additionally, or alternatively, the third and fourth characteristic may relate to another, i.e. a second seat. Further in particular, the first and second characteristic may define a first region associated with the first seat, and the third and fourth characteristic may define a second region, different from the first region, associated with the second seat.

The second seat is, for example, the center seat of the back row. Therein, a third characteristic and a fourth characteristic related to the second seat may be identified in the image, by using the processing device.

In particular, the location of the third characteristic and the location of the fourth characteristic together define the second region and the second region covers a portion of the second seat. In particular, the second region is spanned from the location of the third characteristic to the location of the fourth characteristic. Typically, the second region covers at least a part or the whole of the back rest area of the second seat.

Optionally, the second region is identified additionally by considering the location of the first and/or second characteristic location.

According to an embodiment, a third region in the image is identified based on the second region, wherein the third region covers a portion of a third seat in the cabin. In particular, the third region is identified using the size and/or location of the second region. Further, the third region may be identified in relation to the second region.

The third region relates to a third seat which may be in particular a left or right seat in the back row of the cabin. Typically, the third region covers at least a part or the whole of the back rest area of the third seat.

Through this embodiment it is possible that the third region can be found without the need to identify further characteristics and in particular their locations.

Additionally, a fourth, fifth, sixth and seventh region may be identified. In particular, the fourth region may relate to the other of the left or right set in the back row of the vehicle cabin, covering at least a part or the whole of the back rest region of the fourth seat and a fifth region may relate to the other passenger set in the front row of the vehicle cabin, covering at least a part or the whole of the back rest region of the fifth seat. The sixth and seventh region may relate to the space from the first seat and the fifth seat, respectively, to the edge of the captured image.

In particular, the first, second, third, fourth, fifth, sixth and seventh region may be identified as a first, second, third, fourth, fifth, sixth and seventh region of interest or a first, second, third, fourth, fifth, sixth and seventh crop in the captured image.

According to an embodiment, the first characteristic defines a first corner of the region, either the first region or the second region, and the second characteristic defines a second corner of the region, which is diagonally opposite to the first corner.

In particular, the first region may be a rectangle and the first characteristic defines a first corner of the first rectangle and the second characteristic defines a second corner of the first rectangle. Therein, the first corner of the first rectangle is diagonally opposite to the second corner of the first rectangle.

In particular, the first characteristic indicates the top right corner of the first rectangle and the second characteristic indicates the bottom left corner of the first rectangle.

Alternatively, the first characteristic indicates the top left corner of the first rectangle and the second characteristic indicates the bottom right corner of the first rectangle.

In another embodiment, the first region is a circle, wherein the first characteristic defines a center point of the circle and the second characteristic defines a radius of the circle. Alternatively, the first characteristic and the second characteristic define opposite points on the circumference of the circle, diagonally connected by the diameter.

Thereby, the first region is well-defined by using only two locations. In particular, when considering the outline of the typically rectangle-shaped image, the four edges of the first rectangle resulting from the diagonally opposite first and second corners may be parallel to the edges of the outline of the image.

According to an embodiment, the third characteristic defines a first edge of the region and the fourth characteristic defines a second edge of the region, which is parallel to the first edge.

In particular, the second region may also be a rectangle and the third characteristic defines a first edge of the second rectangle and the fourth characteristic defines a second edge of the second rectangle. Therein, the first edge of the second rectangle is parallel to the second edge of the second rectangle.

In particular, the third characteristic indicates the top edge of the second rectangle and the fourth characteristic indicates the bottom edge of the second rectangle.

Alternatively, the third characteristic indicates the left edge of the third rectangle and the fourth characteristic indicates the right edge of the first rectangle.

In another embodiment, the second region is a circle, wherein the third characteristic defines a center point of the circle and the fourth characteristic defines a radius of the circle. Alternatively, the third characteristic and the fourth characteristic define opposite points on the circumference of the circle, diagonally connected by the diameter.

Thereby, the second region is well-defined by using only two locations. In particular, when considering the outline of the typically rectangle-shaped image, the four edges of the second rectangle resulting from the first and parallel second edge may be parallel to the edges of the outline of the image.

According to an embodiment, the third region has the same size as the second region.

In particular, the third region has the same height and width as the second region. Further in particular, the third region is directly adjacent to the second region.

By defining the third region with the same size as the second region, and in particular by defining the third region as directly adjacent to the second region, the third region can be identified very easily and without consuming much computing power.

In other embodiments, the first region, the second region and/or the third region may have different geometrical shapes, in particular shapes being different from each other. Further in particular, the first, second and third region may have the shape of a polygon or a star or the like.

According to an embodiment, the first characteristic is a seat belt mount of the seat, in particular of the first seat.

The seat belt mount is a mount, typically located on the inside of the B pillar of the vehicle. It is shaped like a bracket, through which the seat belt is guided to be placed on the shoulder of the person sitting in the first seat, in particular the driver's seat. This first characteristic is found in many, if not all cars, and in particular in roughly the same location, and thus can be identified easily in the image.

According to an embodiment, the second characteristic is a seat belt buckle of the seat, in particular of the same first seat.

The seat belt buckle is a fixation means into which the seat belt is inserted and secured on. The seat belt buckle is typically located on the seat side facing the center of the vehicle, usually near the handbrake and thus on the opposite side of the seat as the seat belt mount, and usually at the height of the seat bottom rest. This second characteristic is found in many, if not all cars, and in particular in roughly the same location, and thus can be identified easily in the image.

According to an embodiment, the third characteristic is a top edge of the back rest of the seat. This may be the same first seat or the second seat.

In particular, the third characteristic is the upper horizontal line where the back rest ends. This third characteristic is found in many, if not all cars, and in particular in roughly the same location, and thus can be identified easily in the image.

According to an embodiment, the fourth characteristic is a front edge of the bottom rest of the seat. This may be the same first seat or the second seat.

In particular, the fourth characteristic is the lower horizontal line where the back rest begins from the bottom rest. This fourth characteristic is found in many, if not all cars, and in particular in roughly the same location, and thus can be identified easily in the image.

According to an embodiment, the method further comprises to identify, by means of the processing device, a first sub-region based on the region, in particular the first region, and to identify, by means of the processing device, a type of occupancy of the seat, in particular the first seat, based on the first sub-region.

The first sub-region has a different size, in particular a different height and/or width, and/or is offset from the center point of the first region by a predetermined and/or predefined amount. The first sub-region is particularly adapted to the particular type of occupation of the first seat, for example, a person, a small object or a child's seat. The type of occupancy can in particular be identified by using a classification algorithm on the particular first sub-region.

According to an embodiment, the method further comprises to identify, by means of the processing device, a second sub-region based on the region, in particular the first region, and to identify, by means of the processing device, a type of occupancy of the seat, in particular the first seat, based on the second sub-region.

The second sub-region has a different size, in particular a different height and/or width, and/or is offset from the center point of the first region and/or the first sub-region by a predetermined and/or predefined amount. The second sub-region is particularly adapted to the particular type of occupation of the first seat, for example, a person, a small object or a child's seat. The type of occupancy can in particular be identified by using a classification algorithm on the particular second sub-region.

It goes without saying the further sub-region may be identified with respect to the first region, in particular, a third, fourth and fifth sub-region. Additionally, a first, second, third, fourth and/or fifth sub-region may be identified with respect to the second, third, fourth, fifth, sixth and/or seventh region.

According to an embodiment, the method further comprises to identify, by means of the processing device, a first reference body point in the first sub-region and to identify, by means of the processing device, a type of occupancy of the first seat based on the first reference body point.

In another aspect, the present disclosure is directed at a computer system, said computer system being configured to carry out several or all steps of the computer implemented method described herein.

The computer system may comprise a processing device, at least one memory device and at least one non-transitory data storage. The non-transitory data storage and/or the memory device may comprise a computer program for instructing the computer to perform several or all steps or aspects of the computer implemented method described herein.

<FIG> depicts a view of a computer system <NUM> for detecting an occupancy of a seat within a vehicle cabin. The computer system <NUM> comprises a processing device <NUM>, an imaging device <NUM> and a memory device <NUM>.

The computer system <NUM> is adapted to perform a method for detecting an occupancy of a seat in the vehicle cabin. Therefore, the imaging device <NUM> is adapted to capture an image of the vehicle cabin. The processing device <NUM> is adapted to determine one or more seats in the vehicle cabin by processing the captured image, to identify a set of characteristics associated with each seat in the vehicle cabin in the image, to determine a region associated with each of the detected seats based on the set of characteristics, wherein each region is configured to cover a portion of the seat and to determine a seat occupancy status by processing information obtained from the corresponding region.

The method will be described in greater detail with respect to the following figures.

<FIG> shows a flow chart of a computer implemented method <NUM> for detecting an occupancy of a seat. The results of the steps <NUM> to <NUM> are shown in <FIG>.

Therein, the method starts in step <NUM>, in which capturing, by means of an imaging device, an image <NUM> of a vehicle cabin.

In a next step <NUM>, a first characteristic and a second characteristic related to a first seat in the vehicle cabin in the image are identified. Therein, the first characteristic is a seat belt mount of the first seat and the second characteristic is a seat belt buckle of the first seat.

In a further step <NUM>, a first region being a first rectangle <NUM> in the image is identified, wherein the location of the first characteristic defines a first corner <NUM> of the first rectangle <NUM> and the location of the second characteristic defines a second corner <NUM> of the first rectangle <NUM>, diagonally opposite of the first characteristic. Therein the first rectangle <NUM> covers a portion of the first seat, which is the driver's seat of the front row of the vehicle cabin. As can be seen in <FIG>, the four edges of the first rectangle <NUM> are parallel to the frame or outline of the captured image <NUM>.

In another step <NUM>, a third characteristic and a fourth characteristic related to a first seat in the vehicle cabin in the image are identified. Therein, the third characteristic is a top edge of the back rest of the second seat and the fourth characteristic is a front edge of the bottom rest of the second seat.

In a further step <NUM>, a second region being a second rectangle <NUM> in the image is identified, wherein the location of the third characteristic defines a first edge <NUM> of the second rectangle <NUM> and the location of the fourth characteristic defines a second edge <NUM> of the second rectangle <NUM> parallel to the first edge <NUM>. Therein the second rectangle <NUM> covers a portion of the second seat, which is the center seat in the back row of the vehicle cabin. As can be seen in <FIG>, the four edges of the second rectangle <NUM> are parallel to the frame or outline of the captured image. In particular, the first edge <NUM> of the second rectangle <NUM> is parallel to the top edge of the image <NUM> and the second edge <NUM> is parallel to the bottom edge of the image <NUM>.

In another step <NUM>, a third region being a third rectangle <NUM> is identified in the image based on the second rectangle <NUM>, wherein the third rectangle <NUM> covers a portion of a third seat in the vehicle cabin, which is the right seat in the back row of the vehicle cabin. Therein, the third rectangle <NUM> has the same size as the second rectangle <NUM> and shares the same side edge with the second rectangle <NUM>. In particular, the third rectangle <NUM> is a mirrored form of the second rectangle <NUM> around the right side edge of the second rectangle <NUM>. As can be seen in <FIG>, the four edges of the third rectangle <NUM> are parallel to the frame or outline of the captured image.

In another step <NUM>, a fourth region being a fourth rectangle <NUM> is identified based on the second rectangle, wherein the fourth rectangle <NUM> covers a portion of a fourth seat in the vehicle cabin, which is the left seat in the back row of the vehicle cabin. Therein, the fourth rectangle <NUM> has the same size as the second rectangle <NUM> and the third rectangle <NUM> and shares the same side edge with the second rectangle <NUM>. In particular, the fourth rectangle <NUM> is a mirrored form of the second rectangle <NUM> around the left side edge of the second rectangle <NUM>. As can be seen in <FIG>, the four edges of the fourth rectangle <NUM> are parallel to the frame or outline of the captured image <NUM>.

In a further step <NUM>, a fifth characteristic and a sixth characteristic related to a fifth seat in the vehicle cabin in the image are identified. Therein, the fifth characteristic is a seat belt mount of the fifth seat and the sixth characteristic is a seat belt buckle of the fifth seat.

In another step <NUM>, a fifth region being a fifth rectangle <NUM> in the image is identified, wherein the location of the fifth characteristic defines a first corner <NUM> of the fifth rectangle and the location of the sixth characteristic defines a second corner <NUM> of the fifth rectangle, diagonally opposite of the fifth characteristic. Therein the fifth rectangle covers a portion of the fifth seat, which is the passenger's seat of the front row of the vehicle cabin. As can be seen in <FIG>, the four edges of the fifth rectangle <NUM> are parallel to the frame or outline of the captured image <NUM>.

In a further step <NUM>, a sixth region being a third rectangle <NUM> is identified based on the first rectangle <NUM>, wherein the sixth rectangle <NUM> covers a portion to the right of the first seat in the vehicle cabin. Therein, the sixth <NUM> rectangle shares the same side edge with the first rectangle <NUM>, in particular the right side edge of the first rectangle <NUM> and extends until the right edge of the image, covering the entrance region around the driver's door and the B pillar.

Similarly, in another step <NUM> a seventh region being a third rectangle <NUM> is identified based on the fifth rectangle <NUM>, wherein the seventh rectangle <NUM> covers a portion to the left of the fifth seat in the vehicle cabin. Therein, the seventh rectangle <NUM> shares the same side edge with the fifth rectangle <NUM>, in particular the left side edge of the fifth rectangle <NUM> and extends until the left edge of the image, covering the entrance region around the passenger's door and the B pillar.

The step <NUM>, <NUM> and/or <NUM> may be done in particular by using a trained machine-learning model.

Needless to say, that the order of the steps <NUM> and <NUM>, <NUM> and <NUM>, <NUM>, <NUM>, <NUM> and <NUM>, <NUM> as well as <NUM> may be performed in a different order and/or simultaneously. In particular, it is possible that the steps <NUM> and <NUM> are performed at the same time in a first instance and the steps <NUM> and <NUM> at the same time in a second instance, then step <NUM> followed by <NUM>, then <NUM> and <NUM> at the same time followed by <NUM> and <NUM> at the same time.

In a further step <NUM>, an occupancy of the first, second, third, fourth and/or fifth seat is identified based on the first, second, third, fourth and fifth rectangle <NUM>, <NUM>, <NUM>, <NUM>, <NUM>. Further, by identifying an occupancy of the sixth or seventh rectangle <NUM>, <NUM>, it is possible to detect whether a person enters or exits the vehicle cabin. Detecting the occupancy of a rectangle may be done by comparing the rectangles with empty rectangles of a previously captured image.

After having identified the total of seven rectangles in the captured image, and the occupancies of the first to fifth seat, the method <NUM> can proceed the steps shown in <FIG>.

<FIG> shows a flow chart of a computer implemented method for detecting a type of occupancy of a seat, as shown in conjunction with <FIG>, <FIG>, <FIG> and <FIG>, in particular after previously identifying the rectangles of the seats as shown in <FIG> and <FIG>.

As can be seen from <FIG>, <FIG> and <FIG>, the image <NUM> shows a different type of vehicle cabin, which is why the rectangles <NUM>, <NUM>, <NUM>, <NUM> and <NUM> are at different positions and have different sizes as shown in comparison to <FIG>.

In a first step <NUM>, first sub-regions being sub-rectangles <NUM>' <NUM>', <NUM>', <NUM>', indicated with a dashed line, are identified based on the rectangles <NUM>, <NUM>, <NUM>, <NUM> and <NUM>, indicated with a solid line, as previously identified.

In particular, a first sub-rectangle <NUM>' is identified based on the location and size of the first rectangle <NUM>, a first sub-rectangle <NUM>' is identified based on the location and size of the second rectangle <NUM>, a first sub-rectangle <NUM>' is identified based on the location and size of the third rectangle <NUM>, a first sub-rectangle <NUM>' is identified based on the location and size of the fourth rectangle <NUM> and a first sub-rectangle <NUM>' is identified based on the location and size of the fifth rectangle <NUM>.

Therein, the first sub-rectangles <NUM>' <NUM>', <NUM>', <NUM>' and <NUM>' relate to the identification of a person <NUM>. Therefore, the first sub-rectangles <NUM>' <NUM>', <NUM>', <NUM>' and <NUM>' are larger in size, in particular in height than the respective rectangles <NUM>, <NUM>, <NUM>, <NUM> and <NUM> and/or offset from the center thereof. The type of occupation in the first sub-rectangles, if any, is then identified in a further step <NUM>, in particular by using a classifier algorithm for persons.

In a further step <NUM>, second sub-rectangles <NUM>" <NUM>", <NUM>", <NUM>" and <NUM>", indicated with a dashed line, are identified based on the rectangles <NUM>, <NUM>, <NUM>, <NUM> and <NUM>, indicated with a solid line, as previously identified.

In particular, a second sub-rectangle <NUM>" is identified based on the location and size of the first rectangle <NUM>, a second sub-rectangle <NUM>" is identified based on the location and size of the second rectangle <NUM>, a second sub-rectangle <NUM>" is identified based on the location and size of the third rectangle <NUM>, a second sub-rectangle <NUM>" is identified based on the location and size of the fourth rectangle <NUM> and a second sub-rectangle <NUM>" is identified based on the location and size of the fifth rectangle <NUM>.

Therein, the second sub-rectangles <NUM>" <NUM>", <NUM>", <NUM>" and <NUM>" relate to identification of a child's seat <NUM>. Therefore, the second sub-rectangles <NUM>" <NUM>", <NUM>", <NUM>" and <NUM>" are smaller in height and larger in width than the respective rectangles <NUM>, <NUM>, <NUM>, <NUM> and <NUM> and/or offset from the center thereof. The type of occupation in the second sub-rectangles, if any, is then identified in a further step <NUM>, in particular by using a classifier algorithm for child's seats.

In a further step <NUM>, third sub-rectangles <NUM>"', <NUM>"', <NUM>"', <NUM>"' and <NUM>"', indicated with a dashed line, are identified based on the rectangles <NUM>, <NUM>, <NUM>, <NUM> and <NUM>, indicated with a solid line, as previously identified.

In particular, a third sub-rectangle <NUM>"' is identified based on the location and size of the first rectangle <NUM>, a third sub-rectangle <NUM>"' is identified based on the location and size of the second rectangle <NUM>, a third sub-rectangle <NUM>"' is identified based on the location and size of the third rectangle <NUM>, a third sub-rectangle <NUM>"' is identified based on the location and size of the fourth rectangle <NUM> and a third sub-rectangle <NUM>"' is identified based on the location and size of the fifth rectangle <NUM>.

Therein, the third sub-rectangles <NUM>"' <NUM>"', <NUM>"', <NUM>"' and <NUM>"' relate to identification of a small objects <NUM>, such as a teddy bear. Therefore, the third sub-rectangles <NUM>"' <NUM>"', <NUM>"', <NUM>"' and <NUM>" are smaller in height and/ or in width than the respective rectangles <NUM>, <NUM>, <NUM>, <NUM> and <NUM> and/or offset from the center thereof. The type of occupation in the third sub-rectangles, if any, is then identified in a further step <NUM>, in particular by using a classifier algorithm for small objects.

As can be seen from <FIG> the image <NUM> shows yet again a different type of vehicle cabin, which is why the rectangles <NUM>, <NUM>, <NUM>, <NUM> and <NUM> are at different positions and have different sizes as shown in comparison to <FIG> and <FIG>, <FIG> and <FIG>, respectively.

In a further step <NUM> of the method <NUM>, fourth sub-rectangles <NUM>"", <NUM>"", <NUM>"", <NUM>"" and <NUM>"", indicated by a dashed line, are identified based on the rectangles <NUM>, <NUM>, <NUM>, <NUM> and <NUM>, indicated with a solid line, as previously identified.

In particular, a fourth sub-rectangle <NUM>"" is identified based on the location and size of the first rectangle <NUM>, a fourth sub-rectangle <NUM>"" is identified based on the location and size of the second rectangle <NUM>, a fourth sub-rectangle <NUM>"" is identified based on the location and size of the third rectangle <NUM>, a fourth sub-rectangle <NUM>'''' is identified based on the location and size of the fourth rectangle <NUM> and a fourth sub-rectangle <NUM>"" is identified based on the location and size of the fifth rectangle <NUM>.

Therein, the fourth sub-rectangles <NUM>'''' <NUM>"", <NUM>"", <NUM>"" and <NUM>"' relate to identification of a body points of a person. Therefore, the fourth sub-rectangles <NUM>'''' <NUM>"", <NUM>"", <NUM>"" and <NUM>"' are different in height and/ or in width than the respective rectangles <NUM>, <NUM>, <NUM>, <NUM> and <NUM> and/or offset from the center thereof.

Then, in a further step <NUM>, reference body points are identified in the respective fourth sub-rectangles <NUM>"" <NUM>"", <NUM>"", <NUM>'''' and <NUM>"'. These are not identified with a reference numeral for visibility reasons in <FIG> and will be described with further detail in relation to <FIG>, which shows a detail 500a of the image <NUM> as shown in <FIG>.

In particular, as can be seen from <FIG>, a total of five reference body points, indicated by a dotted line, are identified in the fourth sub-rectangle <NUM>"" of the fourth rectangle, the latter of which is not shown due to visibility reasons. There is a first reference body point <NUM>, relating to the nose of a person, a second reference body point <NUM>, relating to a left shoulder of a person, a third reference body point <NUM>, relating to a left knee of a person, a fourth reference body point <NUM>, relating to a right knee of a person and a fifth reference body point <NUM>, relating to a right shoulder of the person.

The reference body points are all located on the rectangle outline and are connected by dotted lines. In particular, the reference body points correspond to the positions where actual body points of a person sitting in the respective seat would be located.

In addition, in another step <NUM>, body points of the person sitting in the fourth seat are identified. In particular, a first body point <NUM>*, relating to the nose of a person, a second reference body point <NUM>*, relating to a left shoulder of a person, a third reference body point <NUM>*, relating to a left knee of a person, a fourth reference body point <NUM>*, relating to a right knee of a person and a fifth reference body point <NUM>*, relating to a right shoulder of the person are identified in the image. Other body points are also identified, in particular relating to the left arm of the person. However, in this embodiment they are not considered relevant.

Claim 1:
Computer implemented method (<NUM>) for detecting an occupancy of a seat within a vehicle cabin,
the method comprising:
- capturing (<NUM>), by means of an imaging device (<NUM>), an image (<NUM>) of the vehicle cabin;
- identifying (<NUM>), by means of a processing device (<NUM>), a set of characteristics associated with a seat in the captured image (<NUM>), wherein the set of characteristics comprises a first characteristic and a second characteristic defining a region associated with the seat and wherein the first characteristic is a seat belt mount of the seat and the second characteristic is a seat belt buckle of the seat;
- determining (<NUM>), by means of the processing device (<NUM>), the region associated with the identified seat based on the set of characteristics, wherein the region is configured to cover at least a portion of the seat; and
- determining (<NUM>) a seat occupancy status of the seat by processing information obtained from the corresponding region.