Various aspects of this disclosure provide an area occupancy determining device. The device may include a memory configured to store at least one occupancy grid of a predetermined region, and a processor. The processor may be configured to generate the occupancy grid of the predetermined region. The occupancy grid includes a plurality of grid cells, each grid cell framed by respective grid cell frame lines. At least some of the grid cells have been assigned an information about the occupancy of the region represented by the respective grid cell. The processor may further be configured to dynamically update the occupancy grid, thereby successively generating a plurality of updated occupancy grids. Each updated occupancy grid is moved relative to the previous occupancy grid such that an origin coordinate of the updated occupancy grid is positioned on a contact point of grid cell frame lines of adjacent grid cells.

A multi-spectral vehicular system for providing pre-collision alerts, comprising two pairs of stereoscopic infrared (IR) and visible light (VL) sensors, each of which providing acquired image streams from a mutual field of view, which are synchronized to provide stereoscopic vision; a data fusion module for mutually processing the data streams, to detect objects within the field of view and calculating distances to detected objects; a cellular based communication module for allowing communication between the sensors and mobile phones/Infotainment systems of the vehicle. The module runs a dedicated background application that is adapted to monitor the vicinity of the vehicle to detect other vehicles having a similar system; calculate speed and heading azimuth of each of the other vehicles; and provide alerts to the driver of the vehicle whenever other vehicles having a similar system are in a path of collision with the vehicle, based on the calculation and on the speed of the vehicle.

A panoramic image system includes at least two camera modules, at least two display elements, and an image processor. Each of the camera modules takes a rear field of view or side field of view to obtain a view image. Each of the display elements displays each of the view images, and each of the view images is configured to have an overlapping region. The image processor receives each of the view images and pieces each of the view images into a panoramic image according to each of the overlapping regions. Each of the display elements selectively displays the panoramic image partially or displays the panoramic image entirely according to a region of interest by a user's observation. The panoramic image system may be applied to a vehicle-monitoring system to assist the driver in viewing the traffic conditions behind the vehicle.

In an object detection apparatus for detecting a position of an object based on at least measured distances to the object as measurement results by a plurality of ranging sensors, a speed difference calculator calculates, for each of candidate points representing the position of the object, a relative speed difference between the relative speeds of the candidate point acquired from the plurality of ranging sensors. The candidate-point determiner determines that a candidate point of the candidate points, the relative speed difference of which is equal to or greater than a predetermined value, is a virtual image of the object. An object detector detects the position of the object based on positions of the candidate points each determined to be a real image after removal of the candidate points each determined to be a virtual image from all of the candidate points.

Systems and methods related to identifying locations and/or ranges to objects using airborne imaging devices are disclosed. An object tracking system may include a plurality of aerial vehicles having associated imaging devices and a control station. Information related to positions and orientations of aerial vehicles and associated imaging devices may be received. In addition, imaging data may be received and processed to identify optical rays associated with objects within the imaging data. Further, a three-dimensional mapping of the identified optical rays may be generated, and locations or ranges of the objects relative to the aerial vehicles may be determined based on any intersections of optical rays within the three-dimensional mapping.

An apparatus of controlling a region of interest (ROI) in an image and a method for controlling the same are provided to determine a region of interest (ROI) in a forward-view image based on braking information and steering information of the vehicle, and obtain driving assistance information from the ROI image. The apparatus includes an image acquisition device configured to acquire a forward-view image by capturing an image about a forward region of a vehicle, a steering information acquisition device configured to acquire steering information by detecting a steering direction and a steering angle of the vehicle, a braking information acquisition device configured to acquire braking information by detecting a braking or non-braking state and a deceleration of the vehicle, and a controller configured to determine a region of interest (ROI) within the forward-view image based on at least one of the steering information or the braking information of the vehicle.

In various examples, object fence corresponding to objects detected by an ego-vehicle may be used to determine overlap of the object fences with lanes on a driving surface. A lane mask may be generated corresponding to the lanes on the driving surface, and the object fences may be compared to the lanes of the lane mask to determine the overlap. Where an object fence is located in more than one lane, a boundary scoring approach may be used to determine a ratio of overlap of the boundary fence, and thus the object, with each of the lanes. The overlap with one or more lanes for each object may be used to determine lane assignments for the objects, and the lane assignments may be used by the ego-vehicle to determine a path or trajectory along the driving surface.

In various examples, object fence corresponding to objects detected by an ego-vehicle may be used to determine overlap of the object fences with lanes on a driving surface. A lane mask may be generated corresponding to the lanes on the driving surface, and the object fences may be compared to the lanes of the lane mask to determine the overlap. Where an object fence is located in more than one lane, a boundary scoring approach may be used to determine a ratio of overlap of the boundary fence, and thus the object, with each of the lanes. The overlap with one or more lanes for each object may be used to determine lane assignments for the objects, and the lane assignments may be used by the ego-vehicle to determine a path or trajectory along the driving surface.

An image recognition apparatus is provided with: a divider configured to divide a frame image into a plurality of areas, wherein the frame image is obtained by imaging or capturing a scene outside through a window glass on which a wiper is mounted, from an interior of a moving body; a determinator configured to determine whether or not at least one of the wiper and an attached substance to the window glass appears, for each of the plurality of areas; an object detector configured to detect an object included in such an area of the plurality of areas that it is determined that the wiper and the attached substance do not appear; and a tracking device configured to track a tracking target, which is an object included in two or more frame images, on the basis of a detection result of the object detector.

A method for determining a motion state of an object in the surroundings of a vehicle. The vehicle includes at least one vehicle camera for providing image data that represent the surroundings. Movability measures and pieces of quality information that are generated by processing the image data, are read in. The movability measures include generated continuous measured values for detecting moving object pixels, using correspondences, recognized using a correspondence algorithm, between pixels in successive images represented by the image data. Pixel-specific pieces of quality information are generated using the read-in pieces of quality information. The pixel-specific pieces of quality information indicate for each pixel the quality of the correspondences. A dynamic object probability is determined for each pixel, using the movability measures and the pixel-specific pieces of quality information. The dynamic object probability indicates for each pixel the probability of belonging to a moving object or to a static object.