Patent Description:
Forage harvesters are used in agriculture to harvest plants from a field, process them, and unload them by means of an adjustable transfer device into a container of a transport vehicle, which drives on a side or to the rear of the forage harvester. The position of the adjustable transfer device, normally arranged in the form of a spout, can be controlled by an operator by means of inputs on a hydraulic handle and actuators, normally hydraulic cylinders, in order to move the adjustable transfer device into a position in which the crop is unloaded into the container of the transport vehicle, but not onto the ground. Usually, the adjustable transfer device can be rotated around a vertical axis, tilted around a horizontal axis to adjust the height of its outer end, and an end flap can be rotated in order to define the exhaust direction of the crop from the transfer device.

To improve loading efficiencies, automatic solutions that use data on the relative position between the harvesting machine and the container have been proposed for controlling the transfer device. Such solutions can utilize an optical image capture device with an image processing system. However, image-based systems are not always able to identify the container correctly, in particular when a field is opened. For example, when opening a field, the forage harvester harvests a first strip of the field with standing crop on both sides such that the transport vehicle follows the forage harvester from behind. In this situation, the distance between the forage harvester and the container is relatively large. It would therefore be advantageous to develop an improved system and method for controlling the position of the transfer device when unloading material into a container positioned at the rear of the harvester.

<CIT> discloses an adjustable transfer device for unloading processed crop onto a container of a transport vehicle includes a control arrangement with an electronic control unit, among other integrated components.

<CIT> discloses an arrangement for the automatic transfer of agricultural crops from a harvesting machine to a transport vehicle has an image capture device, which optically detects one or more features of the transport vehicle.

<NPL> discusses models for object detection.

The present invention provides a method of controlling a spout arrangement on a transferring vehicle, as defined by Claim <NUM>. The present invention further provides a system for controlling a spout arrangement on a transferring vehicle as defined by Claim <NUM>. A control arrangement comprises an image capture device, mounted on a harvesting machine, which provides an image signal to a target detection module and/or a target tracking module. The target may include the container, a portion of the container, or a vehicle towing the container. The harvesting machine comprises a crop receiving header, a crop processing unit for processing crop received from the header, and an adjustable transfer device for unloading processed crop into the container.

The image capture device, or optical image capture device has a field of view that includes the target in most operating conditions. The image capture device has an image signal output connected to an image processing system. The control arrangement further comprises an electronic control unit connected to an output of the image processing system. At least one actuator for adjusting the position of the adjustable transfer device is controlled by the electronic control unit. In some arrangements, at least one sensor for sensing the actuator-controlled position of the adjustable transfer device has a signal output connected to the electronic control unit.

The electronic control unit is operable to perform or execute the following steps, according to one embodiment:.

Embodiments of the disclosure are described in detail below with reference to the accompanying drawings wherein:.

A combination of two agricultural machines shown in <FIG> comprises a self-propelled harvesting machine <NUM> in the form of a forage harvester (<FIG>) and a transport vehicle <NUM> (<FIG>) in the form of a self-propelled tractor, which, by way of a tow bar <NUM> pulls a trailer <NUM>, which comprises a container <NUM>.

The harvesting machine <NUM> has a frame <NUM>, which is carried by front-driven wheels <NUM> and steerable rear wheels <NUM>. The harvesting machine <NUM> is operated from a driver's cabin <NUM>, from which an operator can see a harvesting attachment <NUM>, in the form of a corn header attachment, which is affixed to an entry channel <NUM> on the front side 10A of the forage harvester <NUM>. Crop plants <NUM> harvested from a field <NUM> by way of the harvesting attachment <NUM> are conveyed to a cutter head <NUM> via a gathering conveyor (not shown) with pre-compression rollers (not shown) located in the entry channel <NUM>. The cutter head <NUM> acts in this embodiment as a crop processing unit for processing the crop plants <NUM> received from the harvesting attachment <NUM>, and hence chops them into small pieces and delivers them to a discharge accelerator <NUM>. A post-processing device <NUM> with two kernel processing rollers (not shown) is located removably in the crop flow between the cutter head <NUM> and the discharge accelerator <NUM>. The post-processing device <NUM> can be moved into an inoperative position if it is not needed, for example for a grass harvest, or entirely removed from the harvesting machine <NUM>.

The harvesting machine <NUM> and the harvesting attachment <NUM> are driven by a combustion engine <NUM>. The crops discharged from the discharge accelerator <NUM> exit the harvesting machine <NUM> to the container <NUM> via an adjustable transfer device <NUM> in the form of a discharge spout <NUM>, which can be rotated around an approximately vertical axis by way of a first actuator <NUM> and can be adjusted at a tilt angle by way of a second actuator <NUM>. The discharge direction can be changed by way of a flap <NUM>, the angle of which can be adjusted by way of a third actuator <NUM>. As shown in <FIG>, the container <NUM> can trail behind, or move alongside the harvesting machine <NUM>. Movement of the adjustable transfer device <NUM> allows the crop to be discharged into the container <NUM> in either orientation.

The transport vehicle <NUM> and the trailer <NUM> with the container <NUM> have a conventional structure. The transport vehicle <NUM> comprises front, steerable wheels <NUM> and rear, driven wheels <NUM>, which are supported on a carrying structure <NUM>, which carries a driver's cabin <NUM>. <FIG> show the harvesting machine <NUM> and the transport vehicle <NUM>, respectively, in side views. The harvesting machine <NUM> drives over the field <NUM> in a forward direction (shown by arrow A) in order to harvest the crop plants <NUM>. The transport vehicle <NUM>, <FIG>, follows behind the harvesting machine <NUM> in the same direction (shown by arrow B). The rear-following orientation occurs when a field <NUM> is opened, for example, and the transport vehicle <NUM> would damage the crop plants <NUM> if it drove alongside the harvesting machine <NUM>. During subsequent passes over the field <NUM>, the transport vehicle <NUM> may drive on a harvested part of the field <NUM> on the left or right side of the harvesting machine <NUM>, in a parallel orientation.

The harvesting machine <NUM> is steered by a driver sitting in the driver's cabin <NUM> or by a steering device, which operates automatically. The transport vehicle <NUM> can be equipped with an automated steering device so as to facilitate movement relative to the harvesting machine <NUM>. The harvesting machine <NUM> could also be any other self-propelling harvesting machine, such as a potato or beet harvester.

In one embodiment, the harvesting machine <NUM> is equipped with a first position-determining device <NUM>, which is located on the roof <NUM> of the cabin <NUM> in the embodiment shown in <FIG>. A first radio antenna <NUM> is also positioned on the roof <NUM>. The transport vehicle <NUM> is equipped with a second position-determining device <NUM>, which is located on roof <NUM> of the cabin <NUM> in the embodiment shown in <FIG>. A second radio antenna <NUM> is also located on the roof <NUM> of the transport vehicle <NUM>.

Now turning to <FIG>, which is a top view of the harvesting machine (forage harvester) <NUM> opening a field by harvesting and chopping the crop plants <NUM> from a section of the field as it moves forward. The forage harvester <NUM> then unloads the chopped material through the spout <NUM> to a container <NUM> (e.g., cart) pulled by the transport vehicle <NUM> (e.g. tractor), which is following behind the harvesting machine <NUM>.

In <FIG>, the distance D of the harvesting machine (forage harvester) <NUM> and the transport vehicle <NUM> can be variable, and the spout <NUM> needs to be adjusted so the material coming out of the spout <NUM> will land in the container <NUM>, based on its trajectory as it exits the spout <NUM>. In addition, the transport vehicle <NUM> path might not be perfectly aligned with the forage harvester10, and the spout <NUM> also needs to be adjusted to take into account the left/right offset.

Now, reference is made to <FIG>, in which among other things, the individual components of the position-determining devices <NUM>, <NUM>, an electronic control unit <NUM>, actuators <NUM>, <NUM>, <NUM> for the adjustment of the adjustable transfer device <NUM> and discharge spout <NUM>, sensors <NUM>, <NUM>, <NUM> for the detection of their actual position and the steering devices of the transport vehicle <NUM> (<FIG>) and the harvesting machine <NUM> (<FIG>) are schematically shown. Electronic control unit <NUM> includes a processor and memory. Operating and executable software are stored in memory and executed by the processor. Sensor <NUM> detects the position of the adjustable transfer device <NUM> around the vertical axis, as adjusted by actuator <NUM>. Sensor <NUM> detects the tilt position of the adjustable transfer device <NUM>, as adjusted by actuator <NUM>. Sensor <NUM> detects the angular position of the flap <NUM>, as adjusted by actuator <NUM>. Some of the above mentioned components are also illustrated in <FIG>.

Now turning to <FIG>, according to one embodiment, the first position-determining device <NUM> is on board the harvesting machine <NUM> and comprises an antenna <NUM> and an evaluation circuit <NUM>, which is connected to the antenna <NUM>. The antenna <NUM> receives signals from satellites of a position-determining system, such as GPS, Galileo, or Glonass, which are supplied to an optional evaluation circuit <NUM>. With the aid of the signals of the satellites, the evaluation circuit <NUM> determines the actual position of the antenna <NUM>. The evaluation circuit <NUM> can also be connected to a correction data-receiving antenna <NUM>, which receives radio waves radiated from reference stations at known locations. With the aid of the radio waves, correction data for the improvement of the accuracy of the position-determining device <NUM> are produced by the evaluation circuit <NUM>. The evaluation circuit <NUM> transmits its position data by way of a bus line <NUM> to a control device <NUM>.

The control device <NUM> is connected via an interface <NUM> to a reception and transmission device <NUM>, which is in turn connected to the radio antenna <NUM>. The reception and transmission device <NUM> receives and generates radio waves, which are picked up and radiated by the antenna <NUM>.

Analogously, the second position-determining device <NUM> is located on board the transport vehicle <NUM>. The second position-determining device <NUM> comprises an antenna <NUM> and an optional evaluation circuit <NUM>, which is connected to the antenna <NUM>. The antenna <NUM> receives signals from satellites of the same position-determining system as the antenna <NUM>, which are supplied to the evaluation circuit <NUM>. With the aid of the signals of the satellites, the evaluation circuit <NUM> determines the actual position of the antenna <NUM>. The evaluation circuit <NUM> is also connected to a correction data-receiving antenna <NUM>, which receives radio waves radiated from reference stations at known sites. With the aid of the radio waves, correction data for the improvement of the accuracy of the position-determining device <NUM> are generated by the evaluation circuit <NUM>.

The first and second position-determining devices <NUM> and <NUM> can be used to locate the container <NUM> in situations where direct observation of the container by the image capture device is not possible by calculating the differential positioning. In alternative embodiments, only the second position-determining device <NUM> is used. In situations where a clear view of the container <NUM> is possible, the system directly estimates of the container <NUM> position. In yet another alternative embodiment, the system uses a combination of at least one position-determining device <NUM>/<NUM> and tracking information from the image processing system <NUM>.

By way of a bus line <NUM>, the evaluation circuit <NUM> transmits its position data to a control device <NUM>. The control device <NUM> is connected via an interface <NUM> to a reception and transmission device <NUM>, which in turn is connected to the radio antenna <NUM>. The reception and transmission device <NUM> receives and generates radio waves, which are picked up and radiated by the antenna <NUM>. By the reception and transmission devices <NUM>, <NUM> and the radio antennae <NUM>, <NUM>, it is possible to transmit data from the control device <NUM> to the control device <NUM> and vice-versa. The connection between the radio antennae <NUM>, <NUM> can be direct, for example, in a permissible radio range, such as citizen's band radio, or something similar, or made available via one or more relay stations, for example, if the reception and transmission devices <NUM>, <NUM> and the radio antennae <NUM>, <NUM> work according to the GSM or the UMTS standard or another suitable standard for mobile telephones.

In some embodiments, the control device <NUM> is connected to a steering device <NUM>, which controls the steering angle of the front, steerable wheels <NUM> of the transport vehicle <NUM>. Furthermore, the control device <NUM> sends speed signals to a speed specification device <NUM>, which, via a variation of the engine rpm of the transport vehicle <NUM> and/or the gear transmission, controls the speed of the transport vehicle <NUM>. Moreover, the control device <NUM> is connected to a permanent storage unit <NUM>.

On board the harvesting machine <NUM>, the control device <NUM> is connected to the electronic control unit <NUM>, which, together with the actuators <NUM>, <NUM>, <NUM> it controls and the sensors <NUM>, <NUM>, <NUM> connected to it, forms a control arrangement for the control of the transfer of the crops from the harvesting machine <NUM> to the container <NUM> of the transport vehicle <NUM>. In some embodiments, the electronic control unit <NUM> can be connected to a steering device <NUM>, which controls the steering angle of the rear, steerable wheels <NUM>. Furthermore, the electronic control unit <NUM> sends speed signals to a speed specification device <NUM>, which, via a variation of the gear transmission, controls the propelling speed of the harvesting machine <NUM>. The electronic control unit <NUM> is also connected to a throughput sensor <NUM>, which detects the distance between the pre-compression rollers in the entry channel <NUM>, with a sensor for the detection of the position of sensing arms <NUM> placed on a divider tip of the harvesting attachment <NUM>; a permanent storage unit <NUM>, via valve devices (not shown) with the actuators <NUM>, <NUM>, and <NUM> and with sensors <NUM>, <NUM>, <NUM>, which respectively detect the position of one of the actuators <NUM>, <NUM>, and <NUM>, and with an optical image capture device <NUM>, which is placed more or less in the middle of the adjustable transfer device <NUM> on its left or right or underside 40A (<FIG>), and during the harvesting operation, is aligned on the container <NUM> and is preferably implemented as a stereo-camera having two lenses <NUM> and two image sensors (not shown) arranged one above the other or side by side. The electronic control unit <NUM> receives the signals from the optical image capture device <NUM> via an image processing system <NUM> that processes the image signals from a signal output of the optical image capture device <NUM> in order to extract the position of features of the container <NUM> within the field of view <NUM> of the optical image capture device <NUM>.

In some embodiments, the electronic control unit <NUM> can be connected to a user interface 140A mounted in the cabin <NUM>. The user interface 140A comprises a display unit <NUM> and a user interface with keys <NUM>, which could also be complemented or replaced by a touch-sensitive display unit 142A. Another user interface 140B with at least one key <NUM> is provided on a hydraulic handle <NUM> (not shown) that is pivotally mounted and coupled with a sensor <NUM> connected to the electronic control unit <NUM> in order to receive manual propelling speed commands by the operator in the cabin <NUM>. Some of the above mentioned components are also illustrated in <FIG>.

Operation of the electronic control unit <NUM> is schematically shown in <FIG> and <FIG>, according to one embodiment. Now turning to <FIG>, after start in S300, i.e. after a harvest operation switch (which might be one of the keys <NUM> or another key, not shown, on a dashboard in the cabin <NUM>) of the harvesting machine <NUM> is switched on, and the operation of the electronic control unit <NUM> is initialized, step <NUM> follows. In step <NUM>, it is checked whether a container search command was received from the user interfaces 140A, 140B (<FIG>), thus from a key <NUM> or a key <NUM> assigned to input the desire of the operator to locate a container <NUM> at a position where it is difficult to locate by the optical image capture device <NUM>. Such a position is, in particular, the position behind the harvesting machine <NUM> and the transport vehicle <NUM>, as shown in <FIG>, since the container <NUM> is relatively far away from the optical image capture device <NUM>. Under certain circumstances, as bad visibility or a container <NUM> having a color similar to the color of the field <NUM>, it can however also be useful and possible to input a container search command when the container <NUM> is alongside the harvesting machine <NUM>.

If the result of step S302 is "no," step S304 follows. In step S304, it is checked whether the adjustable transfer device <NUM> is in a rear unloading position according to the signal of the sensor <NUM>. If this is not the case, step S306 is executed, in which the electronic control unit <NUM> controls actuators <NUM>, <NUM>, <NUM> according to the signal from the optical image capture device <NUM>, processed by image processing system <NUM>. This means that in the image from the optical image capture device <NUM>, features are identified, for example the upper edge <NUM> of the container <NUM> (<FIG>), and the actuators <NUM>, <NUM>, <NUM> are controlled such that the crop flow expelled by the adjustable transfer device <NUM>, hits the interior of the container <NUM>. A feedback for the impact point of the crop plants <NUM> on the container <NUM> can be derived from the image signal from the optical image capture device <NUM>. Further, since the optical image capture device <NUM> is a stereo camera, its signals allow to estimate a distance between the harvesting machine <NUM> and the container <NUM> and the height of the upper edges <NUM> of the container <NUM> over ground, such that the actuators <NUM>, <NUM> and <NUM> can be controlled according to a known kinematic model of the free crop flow downstream the adjustable transfer device <NUM>.

On the other hand, if the result in stepS302 or S304 is "yes," the electronic control unit <NUM> proceeds with step S308. This step and the following ones are used to find a container <NUM> in the image of the optical image capture device <NUM> in difficult cases, such as a rear unloading situation shown in <FIG>, in which it is not easy for the electronic control unit <NUM> to identify the container <NUM> in the mentioned image.

In step <NUM>, the electronic control unit <NUM> calculates a position of an expected point of incidence of the crop flow on the container <NUM>, if it is within the field of view <NUM> of the optical image capture device <NUM> (<FIG>). The calculation first identifies the container <NUM> in the field of view <NUM> in an image captured by the optical image capture device <NUM>. Next, expected points of incident within the container <NUM> are calculated based on the captured image. Thus, if no container <NUM> is in the field of view <NUM> of the optical image capture device <NUM>, the process terminates here and goes back to step S300. If on the other hand a container <NUM> is in the field of view <NUM> of the optical image capture device <NUM>, the position of an expected point of incidence of the crop flow on the identified container <NUM> is calculated, based upon the sensor signal in order to learn the direction of the crop flow after leaving the adjustable transfer device <NUM>, and based on an output signal of the image processing system <NUM>, since the electronic control unit <NUM> needs to know the distance between the harvesting machine <NUM> and the container <NUM> in order to derive the expected point of incidence. The distance between the discharge spout <NUM> of harvesting machine <NUM> (or the machine <NUM> itself, e.g. the rotation point of the discharge spout <NUM> around the vertical axis) and the front edge 19A of the container <NUM> can be derived from the signal of the image processing system <NUM> since the optical image capture device <NUM> is a stereo camera. If the optical image capturing device <NUM> were a monocular camera, the size (pixels) of the near edge of the container <NUM> in the image could be used as an estimate for the mentioned distance. Additionally or instead, the mentioned distance can be derived from position data of the harvesting machine <NUM> using the position-determining device <NUM> and a position of the transport vehicle <NUM> transmitted by the radio antennas <NUM>, <NUM>. The orientation of the spout <NUM> based on position of actuator <NUM>, <NUM>, or <NUM> is used to determine the path of crop flow. These alternative embodiments are discussed in further detail in later paragraphs of this disclosure.

In step S308, the known kinematic model of the free crop flow downstream of the adjustable transfer device <NUM> is applied, like in step S306, to calculate where the crop flow would theoretically intersect top plane of the container <NUM> opening. This position can be calculated in absolute coordinates, for example using the position data from the first position-determining device <NUM>, or in relative coordinates with an origin for example at the rotation point of the adjustable transfer device <NUM> around the approximately vertical axis.

Step S308 is followed by step S310, in which an image of the container <NUM> is shown on the display unit <NUM> together with a symbol <NUM> (<FIG>) representing alignment of spout <NUM> with front edge 19A of container <NUM> that coincides with the calculated expected point of incidence of the crop flow on the container discussed above. In other words, the crop flow will intersect the calculated expected point of incidence when symbol <NUM> is aligned with front edge 19A of container <NUM>. The image can be non-processed, i.e. directly come from the optical image capture device <NUM>, or be pre-processed by the image processing system <NUM> in order to remove unimportant details and to emphasize, for example by adding color or changing brightness, features identified in the image that might resemble the container <NUM>.

In step S312, the electronic control unit <NUM> checks whether a confirmation input was received via an assigned one of the keys <NUM> and/or <NUM> from the user interface 140A, 140B. By depressing the key, the operator in the cabin <NUM> can confirm that according to his or her opinion the symbol in the image on the display unit <NUM> is in an appropriate position with respect to the displayed image of the container <NUM> to fill the container <NUM> with crop (<FIG>). This confirmation input could also be input by means of a touch-sensitive display unit 142A or orally or by a suitable gesture detector. Thus, if the result of step S312 is "no," it can be assumed that the symbol <NUM> shown on the display unit <NUM> is outside the image of the container <NUM>.

In this case, step S314 follows in which the electronic control unit <NUM> can receive adjustment inputs from the user interface (by means of keys <NUM> and/or <NUM>) for adjusting the position of one or more of the actuators <NUM>, <NUM>, <NUM> and thus of the adjustable transfer device <NUM>. The electronic control unit <NUM> thus controls the position of the actuators <NUM>, <NUM> and/or <NUM>. Step S314 is followed again by step S308, in which a new image is taken by the optical image capture device <NUM>, and by step S310, in which the position of the symbol <NUM> in the image on the display unit <NUM> is updated according to the output of the sensors <NUM>, <NUM>, <NUM>, which is now changed due to the movement of one or more of the actuators <NUM>, <NUM>, <NUM>. In the situation where symbol <NUM> is not aligned with front edge 19A of container <NUM> (<FIG>. ), the operator can move symbol <NUM> in alignment with front edge 19A of container <NUM> thereby actuating actuator <NUM> to rotate spout <NUM> into position aligned with container <NUM> for rear unloading in which the symbol <NUM> is located on the display unit <NUM> aligned with the image of front edge 19A of the container <NUM> (<FIG>). The adjustment of the symbol <NUM> to the front edge 19A of the container <NUM> can be performed, if necessary, in the horizontal direction and in the vertical direction, be it simultaneously or subsequently, dependent on the operator's choice or as provided by an automated system.

Another embodiment of the present invention considers the container <NUM> being pulled on a side of the harvesting machine <NUM> where the symbol <NUM> can be adjusted to align with the upper lateral edge of the container <NUM> or a side opening thereof. Other embodiments of the present invention can accommodate container orientations relative to the harvesting machine <NUM>, whether the container is aft, forward, or along-side of the harvesting machine <NUM>, and any feature of the container <NUM> within the field of view of the optical capture device <NUM>.

On the other hand, if the operator has confirmed in step S312 that the symbol <NUM> in the image on the display unit <NUM> is in an appropriate position with respect to the displayed image of the container <NUM> (<FIG>. ) to fill the container <NUM> with crop, step S316 is executed, in which the control unit <NUM> derives at least one feature in the image representing the container. This is relatively easy, since the container <NUM> can be assumed to be in close vicinity to symbol <NUM>. The electronic control unit <NUM> thus uses in step S316 the known position of the symbol and suited features in the vicinity of the symbol <NUM> in the image. The electronic control unit <NUM> can identify the upper edges 19A of the container <NUM> in the image. The identified feature is preferably highlighted in the image on the display unit <NUM>, for example by color or brightness. In embodiments where symbol <NUM> is not used, tracking can be accomplished by using the relative position of the container.

In the following step S318, the electronic control unit <NUM> tracks the container <NUM> within the output signal of the image processing system <NUM> based on the image feature retrieved in step S316 and controls the actuators <NUM>, <NUM>, <NUM> in a suitable manner, as described with respect to step S306, in order to fill the container <NUM> with the harvested crop without spilling significant amounts of crop onto the ground. In step S318, actual images can be shown on the display unit <NUM> (<FIG>), like in step S310, in order to inform the operator about the position of the container <NUM> as detected by the electronic control unit <NUM> (preferably highlighting the detected and tracked feature of the container <NUM>) and the expected location of the crop impact point on the container <NUM> by means of the symbol <NUM>.

The embodiment depicted in <FIG> can be summarized as follows:.

Now turning to <FIG>, the embodiment depicted in this figure can be summarized as follows:.

It will become apparent that various modifications can be made without departing from the scope of the invention. For example, one or more functions of the electronic control unit <NUM> can be provided by separate electronic control units, not shown. In steps S306 and S318, control of the adjustable transfer device <NUM> can be augmented according to a relative position of the container <NUM> with respect to the harvesting machine <NUM> derived from position data of the harvesting machine <NUM> using the position-determining device <NUM> and a position of the transport vehicle <NUM> transmitted by the radio antennas <NUM>, <NUM>.

Now turning to <FIG> to illustrate one embodiment of the tracking processes, when symbol <NUM> is used, of the present invention for rear unloading.

Block <NUM>: Indicator <NUM>, such as cross hairs (<FIG>), are aligned with the front edge 19A of the container <NUM>.

Block <NUM>: Stereo Camera captures the salient features from the video (<FIG>) and <NUM>-D data near the front edge 19A of the container <NUM> and use it as the tracking template.

Block <NUM>: The relative location of the front edge 19A of the container with respect to the forage harvester <NUM> is computed from the <NUM>-D stereo measurement of the salient features. The salient features of the front edge 19A are identified automatically by the tracking algorithm based on unique appearance or shape.

Block <NUM>: The horizontal direction, tilt and flap of the discharge spout is adjusted based on the relative location of the front edge 19A with respect to the forage harvester <NUM>.

Block <NUM>: A check is performed whether the rear unloading process is terminated by the operator. If the check is "Yes," then the process continues to Block <NUM> and the procedure to done. If the check is "No," then the process continues to Block <NUM> to capture a new image from the camera of the container and the process returns to Block <NUM> for continued processing.

In alternative embodiments, a target tracking module <NUM> tracks the container <NUM> without performing step <NUM>. That is, tracking occurs without using the crosshair symbol <NUM> as a starting point for finding the container <NUM>. In yet another alternative embodiment, the receiving vehicle <NUM> and not the container <NUM> is tracked.

<FIG> shows an alternative embodiment of the present invention that utilizes position measuring devices on both the harvesting machine <NUM> and transport vehicle <NUM> to assist the optical image capture device <NUM> in measuring the relative motion between the harvesting machine (i.e. forage harvester) <NUM> and transport vehicle <NUM> of <FIG>.

Block <NUM>: An indicator <NUM>, such as crosshairs, is aligned with the front edge 19A of the container (<FIG>) and spout <NUM> is adjusted to align with the front edge 19A of the container <NUM> as discussed above.

Block <NUM>: A stereo camera <NUM> (<FIG>) captures the salient features from the video and 3D data near the front edge 19A of the container <NUM> and uses it as the tracking template.

Block <NUM>: A series of steps are performed:.

Block <NUM>: Start Unloading Material, if not already started;
Block <NUM>: New GPS coordinates of the forage harvester <NUM> and transport vehicle <NUM> are received and combined with the relative location X<NUM> of the container <NUM> with respect to the transport vehicle <NUM> to get the relative location (X<NUM> + X<NUM>) of the container <NUM> with respect to the forage harvester <NUM>.

Block <NUM>: Align cross hair of indicator <NUM> and spout <NUM> accordingly to maintain accurate discharge of the material into container <NUM>.

Block <NUM>: A check is performed to determine whether the rear unloading process is terminated by the operator. If check is "Yes," then the process continues to Block <NUM>. If check is "No," then the process returns to Block <NUM> to continue the process.

In alternative embodiments, control of the adjustable transfer device <NUM> is accomplished without confirming the expected point of incidence of the crop within the container <NUM>. In these alternative embodiments, the control arrangement localizes the receiving container <NUM> in the harvester surroundings. The identified pose of the container <NUM>, whether determined directly or indirectly, is used to calculate the target location commanded to the harvester <NUM> in order to deposit the crop material into the container <NUM>.

Two techniques can be used for estimating the receiving container <NUM> position-direct estimation and indirect estimation. In a first technique, the control arrangement directly tracks the position of the receiving container <NUM> by receiving data about the position and/or pose of the container <NUM>. In one embodiment of the direct tracking technique, onboard sensors (such as an optical image capture device <NUM>) on the harvester <NUM> provide data about the position of the receiving container <NUM> relative to the harvester <NUM>. In this embodiment, the image capture device <NUM> requires visibility of the receiving container <NUM>. Alternatively, the image capture device <NUM> provides data about the receiving vehicle <NUM>, which is pulling the container <NUM>.

In a second embodiment, the receiving container <NUM> reports its own position to the harvester <NUM>. The relative position is then determined using the harvester's onboard GPS sensor (or first position-determining device <NUM>) or its odometry based pose. The receiving container <NUM> can determine its position in multiple ways depending on the embodiment. For composed articulated receiving vehicles <NUM>, the position of the container <NUM> can be derived from the pose of the pulling segment (for example, the tractor or truck cabin) and a sensor <NUM> on the receiving vehicle <NUM> or container <NUM> that calculates a relative offset between pulling vehicle <NUM> and container <NUM>.

In a second technique for estimating the receiving container <NUM> position, the control arrangement indirectly tracks the container <NUM> by tracking the receiving vehicle <NUM> position and extrapolating the trajectory of the container <NUM> being pulled by the receiving vehicle <NUM>. In alternative embodiments, a hybrid approach is used where the position of the container <NUM> is indirectly estimated and partial corrections are made to the indirect estimation based on direct observations when available.

Using the hybrid approach, an example embodiment of the control arrangement comprises a target detection module <NUM>, a target tracking module <NUM>, a kinematic module <NUM>, and a position filter <NUM>. Further, the hybrid approach utilizes a container heading estimator <NUM> and a receiving vehicle articulated modeling module <NUM>. While these modules are described in terms of an embodiment using the hybrid approach of estimating the container <NUM> position, certain of the modules are used in the direct and indirect estimation technique as well. For example, an embodiment using the direct estimation technique with a sensor <NUM> utilizes the target detection module <NUM>, the target tracking module <NUM>, the kinematic module <NUM>, and a position filter <NUM>. A direct tracking embodiment using position-determining device <NUM> and/or <NUM> uses the kinematic model <NUM> and position filter <NUM>, but not the detector module <NUM> and tracking module <NUM>. For indirect tracking, one example embodiment uses the aforementioned modules in addition to processing data regarding the articulation of the container <NUM>.

<FIG> is a simplified system diagram illustrating the hybrid approach. First, the target detection module <NUM> receives image data from the image capture device <NUM> or the image processing system <NUM>. Optionally, the target detection module <NUM> receives data about the spout <NUM> position from the kinematic module <NUM>. As will be discussed in more detail, the target detection module <NUM> identifies a region in the image data that likely corresponds to the target, which can be the receiving vehicle <NUM> or container <NUM>. This information is sent to a target tracking module <NUM>, which determines the pose trajectory of the target in the scene. The pose trajectory is sent to both the container heading estimator <NUM> and receiving vehicle articulated module <NUM>, which help improve the accuracy of pose trajectory in certain situations. The current pose trajectory is then sent to a position filter <NUM>. Finally, the trajectory is used by the electronic control unit <NUM> to control the position of the spout <NUM>. The electronic control unit <NUM> uses the data, in addition to data provided by the kinematic module <NUM> and material projection module <NUM> to determine the expected crop incidence point.

In this disclosure, any module, estimator, filter, generator, evaluator, or optimizer may comprise a controller, a microcomputer, a microprocessor, a microcontroller, an application specific integrated circuit, a programmable logic array, a logic device, an arithmetic logic unit, a digital signal processor, or another data processor and supporting electronic hardware and software. Further, the modules may be standalone unit or may be part of the electronic control unit <NUM>.

Table <NUM>, shown below, demonstrates different embodiments of direct, indirect, and hybrid tracking techniques and the modules used in each embodiment. The embodiments listed in Table <NUM> are examples and different combinations of modules can be used depending on the desired performance and other parameters.

The target detection module <NUM> localizes an object of interest (such as a tractor or driver's cabin for indirect tracking or the container <NUM> for direct tracking) in image data provided by the image capture device <NUM> or image processing system <NUM>. Stated differently, the target detection module <NUM> uses the image data of the harvester's surroundings as input and produces an estimate of the target object's position and extent as output.

To provide the target object's position, the target detection module <NUM> first generates a region of interest in the image data using a candidate generation module <NUM>. The region of interest candidate generation module <NUM> selects regions of the image that could contain the target. The module <NUM> uses the relative position of the image capture device <NUM> to the scene to model the prior knowledge of the most likely positions of the target based on the current mode of operation. That is, the candidate generation process is sensitive to the position of the image capture device <NUM> relative to the target. In one embodiment, the image capture device <NUM> is mounted on the spout <NUM> and, thus, its position is reported by the kinematic model <NUM>. The location of the horizon is determined, which is used to constrain the search area. Similarly, a model of the harvester extent defines an exclusion zone where the target cannot be located.

In addition to geometric data about the scene, operation settings defined by the user (such as the type of the receiving vehicle configuration) will help estimate the base dimensions of the search area. In an alternative embodiment, the candidate generation module <NUM> includes a trainable component that adjusts the region of interest dimensions based on features extracted from the input sensor data, such as local height per region. By using geometric data and scene features, the computational costs are reduced.

Once a region of interest is generated, a target discrimination module <NUM> analyzes the region of interest provided by the region of interest candidate generation module <NUM> to classify areas in the region of interest as background areas or target areas (for example, areas where the container <NUM> is located).

In one embodiment, a simple window-scanning approach is used to analyze the image data. More specifically, engineered features extracted from predefined windows of the input are discriminated using a linear classifier. In order to accelerate the feature extraction process, integral channel descriptions are used when possible. When a stereo camera is used for the image capture device <NUM>, range information can be used to incorporate geometry-based features to the set of descriptions, which increases the precision of the classifier with respect to methods only using appearance information.

The feature computation is organized in two possible sequential slots or channels. This scheme simplified sharing features in one of the channels with other stages (for example, region of interest generation), while optionally evaluating the other channel if required depending on the input sample, providing significant computational savings when providing real-time target detection.

Table <NUM> (shown below) presents different combinations of features that can be used for target discrimination. These features are complementary in term of discriminative power and robust to different degenerations in the input sensor data (e.g. dusty image conditions or errors in the image capture device position). In one embodiment, the feature set includes the Histogram of Oriented Gradients (HOG), a descriptor commonly applied in image, depth, and height space. The input features block-normalized for best classification behavior.

In one embodiment, the target discrimination module <NUM> uses a simple linear Support Vector Machine (SVM) classifier. However, in alternative embodiments, more complex state-of-the-art models can be used, depending of the performance and computation requirements of the control arrangement. Since all of the inputs features are normalized, the linear scheme supports dynamic short-circuiting, allowing the model to only compute a portion of the detection score. This "lazy evaluation" technique is commonly used to optimize machine learning pipelines and avoids the computation of extra features when an input windows is clearly classified as part of the background after only evaluating the first feature channel.

In the example embodiment described above, a multi-staged system (candidate generation module <NUM> and target detection module <NUM>) is used due to its simplicity and reduced computational resources. However, combined algorithms based on deep models can be used in alternative embodiments.

With a multi-stage system, the overall localization accuracy/ detection rate can be optimized while reusing many of the intermediate results. For example, the geometrical features computed by the candidate generation module <NUM> are reused as part of one of the features channels. In this manner, the candidate generation module <NUM> focuses on localization and a low false negative rate, while the target detection module <NUM> focuses on a low false positive rate. To reduce false positives, a non-maxima suppression algorithm incorporates some prior information on the position of the container <NUM> to compensate biases in the detection process, such as conflicting and overlapping detections.

In the last stage of target detection, a detection filter <NUM> aims to provide consistency in the detections by rejection outliers. In many situations, providing consistency requires large datasets for comparison that cannot always be stored. To overcome this issue, in one embodiment, an out-of-core incremental and iterative stochastic gradient descent algorithm is used. In this embodiment, not all samples have to be concurrently loaded or even fit into memory, which allows training with far more data than some existing libraries might support. However, a person having skill in the art will appreciate that other filters can be used to reduce the rate of false-positives.

As described, the target detection module <NUM> is capable of detecting the receiving vehicle <NUM> or container <NUM>, regardless of its physical configuration. Further, beacons or manual interaction is not required. The module <NUM> provides low false detection rates, using an active attention focus mechanism (candidate generation module <NUM>) that uses information such as the current operation status or spout position, as a guide for the selection of the region of interests in the input sensor data most likely to contain the target. Additionally, the target detection module <NUM> provides high discriminative power in different scenarios, including poor texture conditions and cluttered environments, by using image and geometry features. Lastly, an iterative out-of-core training procedure provides improved false-positive rates by preventing overfitting compensation using implicit and hard negative mining tuning.

Once a target is detected, a target tracking module <NUM> determines the pose trajectory of the target, such as the receiving vehicle <NUM> or the container <NUM>. In one embodiment, the tracking module computes trajectory with four degrees of freedom (3D position plus heading), together with the dimensions of the target (length, width and height), using the input data from the image capture device <NUM>. In one embodiment, the tracking module <NUM> determines the optimal pose of the target by minimizing the differences between each input sensor data frame and a projected target pose. Differences are evaluated using a cost function defined by the photometric error (differences in pixel intensities) and, optionally, geometrical error (differences in pixel depth). In one embodiment, the cost function is minimized by taking an analytical derivative from the re-projected points, or, in an alternative embodiment, through a stochastic exploration of the solutions space where pose compositions are non-deterministically evaluated against the template.

The tracking module <NUM> comprises a target modeling module <NUM>, a candidate pose generator <NUM>, and an error function evaluator <NUM>. The target modeling module <NUM> builds a model of the tracked vehicle (i.e. receiving vehicle <NUM> or container <NUM>). The first iteration of the model is initialized to contain the point cloud enclosed by the target detection computed by the target detection module <NUM>. The model is populated with all points within a certain depth and lateral distance of the initialization point, from the perspective of the target's frame. In one embodiment, the points in this region had been previously preprocessed using a median filter on the depth component to provide invariance to stereo noise. After populating, the model is then validated by assessing quality checks to confirm that a valid target has been detected and tracking is viable. For example, a valid target is detected if minimum point cloud dimensions are present and tracking is viable if the target is located within an acceptable distance range. However, other quality checks can be used.

After the first initialization, the target modeling module <NUM> incrementally refines the model in subsequent image frames to account for slow variations in the target template and to reflect the full extent of the tractor <NUM> as its segmentation is completed. For example, in subsequent frames, the model incorporates points from new view angles. In one embodiment, candidate points are incorporated into the model by applying a 3D flood-fill operation around the location of the target predicted by an estimation of the motion of the target. These candidates are then validated against the template point cloud. Points that align between the observed cloud and the prior model are used to update the model.

One challenge of the modelling process is accurately segmenting the tractor from other elements when the scene is cluttered (e.g. presence of high crop in the near proximity of the target). To assist this process, in one embodiment, weak symmetry and dimension constraints are used to restrict the incorporation of new points to the model. This prevents tracking from biasing towards the adjacent crop when the target model erroneously incorporates points outside the vehicle. In yet another embodiment, an elevation map and restrictions given by the size and orientation of a model bounding box are used to constrain segmentation. Low locations are likely to belong to the ground and can be potentially discarded. Higher locations have to be checked against the model bounding box to discard points belonging to the crop or the receiving container, if the receiving vehicle <NUM> is being tracked.

Once the target is modeled, the candidate pose generator <NUM> determines the absolute pose of the target. After the first initialization, the candidate pose generator <NUM> uses relative motion in the target to disambiguate its absolute pose. Although the XYZ position of the target can be directly inferred from the location of the initialization point, determining the heading requires estimating the direction of travel for the target. The heading is computed using an Extended Kalman Filter (EKF), including a simple vehicle motion model. Once target motion is detected and the direction of travel is estimated, the pose generator <NUM> aligns the dominant axis of the target template box and uses the resulting pose as reference for the next frame computation.

Upon receiving the next frame or set of data, the pose generator <NUM> composes incremental variations to the pose computed on the previous frame until it converges on the optimal value describing the current pose. As previously discussed, in one embodiment this process is performed analytically by following the direction of the cost function gradient. Alternatively, the process is performed by a stochastic process. The EKF provides a prediction of the location of the model in the current frame which can be used as a starting point of the optimization or help guiding the stochastic search. Once the pose is deemed sufficiently accurate, the pose generator <NUM> provides the results to the other modules or components in the control arrangement. In one embodiment, the results are passed to an error function evaluator <NUM>, which analyzes the photometric error of the model projected into the current image frame. Alternatively, geometric error can also be used.

With the target detected and tracked, a kinematic module <NUM> is used to determine the current coordinates and orientation of the spout <NUM>. The orientation of the spout <NUM> comprises rotation angle, tilt angle, and the angle of spout flap <NUM>. As shown in <FIG>, the spout <NUM> parameters are inputs to the kinematic module <NUM>. In order to perform this computation, the kinematic module <NUM> stores parameters specific to different types of spouts <NUM> and or adjustable transfer devices <NUM>. The parameters contain information about specific dimensions and structure of the spout <NUM> and or adjustable transfer device <NUM>. In one example embodiment, the specific spout <NUM> type installed on a harvester <NUM> is sent to the kinematic module <NUM> via the CAN bus <NUM>. Knowing the type of spout <NUM> installed, the kinematic module <NUM> determines which spout parameters are applicable. Also transmitted via the CAN bus <NUM> are the current spout <NUM> rotation angle, title angle, and flap <NUM> angle.

With this information, the kinematic module <NUM> can determine the location of the point where the crop material exits the spout <NUM>. Further, the kinematic module <NUM> determines the exit angle of the crop material exiting the spout <NUM>. With this information, the material landing point can be calculated using a material projection module <NUM>. In one embodiment, the landing point is estimated using a parabolic trajectory.

The material projection module <NUM> calculates the landing point by using the crop material exit point and exit angle from the kinematic module <NUM> in addition to other parameters such as material initial velocity (preprogrammed or from a machine controller via CAN bus <NUM>), crop type, crop moisture, mass flow, wind speed, wind direction, machine roll, and machine pitch. As shown in <FIG>, these parameters are inputs to the material projection module <NUM>. Depending on the extent to which these parameters affect the crop flight, the projection module <NUM> can make an assumption about the deceleration of the crop material from the exit point to the landing point. With the landing point of the crop determined and the location of the container <NUM> known, the control arrangement can cause changes in the adjustable transfer device <NUM> to accurately place the material in the desired location of the container <NUM>. Further, an iterative process is used to compare the current material landing point to the desired landing point. Once the error between the actual and desired landing point is calculated, the spout <NUM> angles are adjusted until the difference between actual and desired material landing point is less than a given threshold.

In the hybrid estimation approach, the receiving container heading estimator <NUM> and receiving vehicle articulated module <NUM> are further used to supplement position estimation.

The receiving container heading estimator <NUM> computes the orientation (in the horizontal plane) of the receiving container <NUM> from the image data. The estimator <NUM> complements and corrects the receiving container pose indirectly estimated by the receiving vehicle articulated module <NUM>.

Like the target tracking module <NUM>, this module determines the pose of the target. However, there are differences between the two. In the estimator <NUM>, heading estimation is limited to a single degree of freedom for the container <NUM> orientation (versus 4D pose) because this parameter is harder to predict using data from the receiving vehicle articulated module <NUM>. The estimation is even more difficult when there is limited or erratic motion while starting the harvest operation.

In one embodiment, the receiving container heading estimator <NUM> comprises a feature extractor <NUM>, a heading candidate module <NUM>, and an optimizer <NUM>. The feature extractor <NUM> identifies descriptors based on the appearance and geometrical information contained in the image data. The heading candidate module <NUM> explores the space of possible heading solutions, computing for each candidate angle a metric representing the probability of matching the expected value from the pre-computed image features of the target. The optimizer <NUM> determines the most likely heading maximizing the compatibility metric.

The feature extractor <NUM> assumes that the container <NUM> can be identified by two distinct features: strong edges or discontinuities in the pixel intensities representing the top opening of the container <NUM>; and differentials in height with respect to adjacent elements, such as the crop. As such, the feature extractor <NUM> creates edge maps and density of height histograms from the image data. Prior information can be encoded in this feature extraction process to increase the robustness of the feature extractor <NUM>. For example, the extractor <NUM> filters out edge features if they do not lie in a plausible range of heights, given prior information on specific container dimensions.

The heading candidate module <NUM> determines which receiving container orientation most closely aligns with the received image data. More specifically, the module <NUM> generates hypotheses of what the captured scene would look like if the container had orientation x, then identifies the value of x that most closely represents the captured scene based on a highest compatibility score to the features extracted from the actual input data.

Accordingly, the heading candidate module <NUM> first defines a set of candidate poses for the receiving container in the scene by invoking the articulated module <NUM> with a given range of heading values as input. The range of heading values is generated by composing incremental offsets to the last computed orientation in the previous frame. Next, the heading candidate module <NUM> computes the projections of the receiving container <NUM> in the input data frame for each of the candidate poses. Finally, the module <NUM> compares each candidate projection with the features computed in the actual input image data and generates a compatibility score for each of them.

In the case of the edges features, the module <NUM> synthesizes the line pattern that the top rectangular opening of the container <NUM> would generate for a given orientation. To assess the similarity of these edge patterns while being robust to background elements (e.g. shadows in adjacent crop), a compatibility score is defined such that it evaluates both spatial and orientation coherence. For example, in one embodiment, the perimeter of the container <NUM> opening is first expanded by a certain margin (to account for inaccuracies in the base position) and then projected into the input image. The projection is then divided into four quadrants and a compatibility metric is computed as the percentage of edge pixels with an orientation similar to the corresponding border of the receiving container <NUM>, relative the total number of pixels. Testing only against a particular orientation and spatial location offers immunity to other background elements while avoiding complex edge computation processes to filter background or unconnected pixels. There is a certain tolerance in the edge orientation comparison allowed by operating in a binarized space, and ultimately this edge orientation computation can be reused with other stages of the system, such as HOG features.

For height features, the container <NUM> height range is inferred dynamically relative to the distribution observed on the input data. Certain distribution of heights are assumed to belong to various elements of the image (such as the ground, vehicles <NUM> and <NUM>, and container <NUM>). Consequently, the compatibility metric is defined as a function of the height distributions in each heading proposal. For noise invariance, the score uses a histogram of heights instead of the raw values. That is, the score is an engineered (nonlinear) combination of the counts within bins deemed on target, below-target, and above-target. The inferred range can be biased by scene elements that are at the same approximate height of the container <NUM>. The situation can occur when the container <NUM> is adjacent to a tall crop segment. In one embodiment, calibration is done to account for tall crops or other similar height objects.

The optimizer <NUM> determines the optimal heading that lies at the maximum of the compatibility-metric curve. The optimizer <NUM> has to be robust to outliers and avoid producing solutions without enough evidential support (e.g., situations with bad visibility). In the case of edge features, for example, the optimizer <NUM> keeps track of the absolute number of edge pixels with the expected orientation for each candidate heading. When the number of such pixels supporting the best candidate orientation is below an absolute threshold (which can be empirically determined in testing sets), the optimizer <NUM> does not return a solution.

In one embodiment, for height features the optimizer <NUM> does not consider the height bins directly, but a Kernel-Density Estimator (KDE) produced from the samples drawn from the image. Critical points in the derivative of the KDE indicate whether the proposed solution is a spurious local maximum or represents a feasible heading. Thus, the container heading estimator <NUM> is able to determine the receiving vehicle heading from the image data.

The receiving vehicle articulated module <NUM> models the structure of the receiving vehicle <NUM>. In general, the vehicle <NUM> is composed of two connected parts, the pulling element (tractor/truck cabin) and the container <NUM>, which can comprise a cart, trailer, or truck bed. Using this module <NUM>, the operator can query for the pose and dimensions of the receiving container <NUM> part using a set of adjustable parameters (such as the type of the receiving vehicle <NUM>) and dynamic inputs (such as the position of the tractor/truck and heading offset to the receiving container <NUM>).

The specific set of user-selectable parameters passed to the articulated module <NUM> is chosen as a trade-off between generality (being able to represent all possible receiving vehicles <NUM> found during operation) and simplicity (limiting the amount of time the operators require to configure the system before operation). In one embodiment, a uniform physical model for all possible receiving vehicles <NUM> is used. The uniform model has a library of default internal settings selectable by a single parameter observable by the operator, such as the type of receiving vehicle <NUM>.

The receiving vehicle position filter <NUM> estimates the position and/or heading of the receiving container <NUM> over time. The filter <NUM> ensures a smooth trajectory and optionally provides pose predictions in the immediate future based on the vehicle kinematics. To accomplish this prediction, the filter <NUM> uses instantaneous pose information from the harvester <NUM> and the estimated relative position to the receiving vehicle <NUM> as and input, together with the configuration of the receiving vehicle <NUM> specified by the articulated module <NUM>.

In one embodiment, the heading offset between receiving vehicle <NUM> and the container <NUM> is unknown and estimating the container <NUM> pose is more difficult than referring to the articulated module <NUM>. In this situation, the filter <NUM> determines the container post, in part, by using the harvester <NUM> trajectory as a reference. The trajectory can be specified using the location device <NUM> or using onboard vehicle odometry. In one embodiment, the trajectory is determined from the linear and angular speeds provided by the location determining receiver <NUM>. As described, the filter <NUM> indirectly infers the pose of the receiving container <NUM> and extrapolates predictions by combining the trajectory of the receiving vehicle <NUM> and the harvester <NUM>.

While the disclosure has been described in detail and with reference to specific embodiments thereof, it will be apparent to one skilled in the art that various changes and modifications can be made therein without departing from the scope of the embodiments. Thus, it is intended that the present disclosure cover the modifications and variations of this disclosure provided they come within the scope of the appended claims and their equivalents.

Claim 1:
A method of controlling a spout arrangement on a transferring vehicle comprising:
capturing image data related to a target being located to a rear of the transferring vehicle using an image capture device (<NUM>), which is mounted to the spout arrangement, wherein the image data contains a scene within a field of view <NUM> of the image capture device (<NUM>);
receiving information about the spout arrangement position;
determining the position of the image capture device (<NUM>) based on the spout arrangement position;
generating a region of interest in the image data that likely corresponds to the target by constraining a search area of the scene by determining the location of the horizon based on the position of the image capture device (<NUM>), and defining an exclusion zone based on a model of the extent of the transferring vehicle;
analyzing the region of interest to classify areas in the region of interest as background areas or target areas; and
determining the position of the target relative to the transferring vehicle using the target areas;
calculating a material trajectory exiting the spout arrangement to determine a material incidence point; and
adjusting the spout arrangement to align the material incidence point with the target position.