Disclosed are a vehicle path restoration system through sequential image analysis which includes: an image capturing unit that acquires sequential images from the front camera installed in the subject vehicle; an image analysis unit for generating multiple lanes that can be recognized from the sequential images of the video file acquired by the image capturing unit and multi-paths calculated using the geometric characteristics of the lanes recognized at the current time and the speed of the subject vehicle, that restores the path of the subject vehicle and restores the path of the front vehicle driving in front of the subject vehicle; a memory for storing path data of the subject vehicle and the front vehicle restored by the image analysis unit; and a display unit that expresses the path data of the subject vehicle and the front vehicle stored in the memory in the form of a top view.

An abnormality detection apparatus including a feature extraction circuit configured to extract a feature point and a feature value of a first image, and a feature point and a feature value of a second image, a flow calculation circuit configured to calculate, based on the feature value of the first image, a first abnormality detection circuit configured to detect an abnormality in the first image based on a first optical flow, and to detect an abnormality in the second image based on a third optical flow, and a second abnormality detection circuit configured to detect an abnormality in the first or second image based on a result of a comparison between the second optical flow and a fourth optical flow.

A water area object detection system includes a first imager to image an object around a hull, a second imager provided on the hull such that an imaging direction of the second imager is the same or substantially the same as an imaging direction of the first imager and operable to image the object around the hull, and a controller configured or programmed to perform a control to create a water area map around the hull based on images captured by the first imager and the second imager. The second imager is spaced apart in an upward-downward direction of the hull from the first imager, and the first imager is spaced apart in the imaging direction from the second imager so as not to overlap the second imager in the upward-downward direction perpendicular to the imaging direction.

A method of operating at least one image sensor that records an image content with a recording direction. The method includes the following steps: a) determining at least one piece of information about an expected main direction of movement of an expected movement in at least a portion of an image content to be recorded relative to the image sensor, b) setting the recording direction taking into account the expected main direction of movement, c) recording at least a portion of the image content with the recording direction.

A method for calibrating a multiplicity of cameras on a vehicle in a common coordinate system. The method includes: a) acquiring camera images using each camera of the multiplicity of cameras, b) ascertaining overlap regions of camera images acquired in step a), c) setting up a common optimization function, which describes the alignments of each camera of the multiplicity of cameras as a target variable of the optimization, based on the overlap regions, d) solving the common optimization function set up in step c) to ascertain the alignments of each camera.

A method includes: accessing a floorplan representing the floorspace; and extracting from the floorplan a set of floorplan features representing areas of interest in the floorspace. The method also includes, calculating a set of target locations relative to the floorplan that, when occupied by the set of sensor blocks: locate the areas of interest in the floorspace within fields of view of the set sensor blocks; and yield a minimum overlap in fields of view of adjacent sensor blocks in the set of sensor blocks. The method further includes, for each sensor block in the sensor blocks installed over the floorspace: receiving, from the sensor block, an image of the floorspace; based on overlaps in the image with images from other sensor blocks in sensor blocks, estimating an installed location of the sensor block; and mapping the sensor block to a target location in the set of target locations.

An image detection method determines a target object. A plurality of original images of a scene in front of a vehicle are obtained. An object in one original image is detected, and a degree of similarity between the object in the original image and the target object in a preset image is calculated. If the degree of similarity is greater than a preset similarity threshold, it is determined that the original image is a target image and the object is the target object. A position of the target object relative to the vehicle is determined and output. The method can recognize objects of interest in front of a driver.

This invention provides an easy-to-manufacture, easy-to-analyze calibration object which combines measurable and repeatable, but not necessarily accurate, 3D features—such as a two-sided calibration object/target in (e.g.) the form of a frustum, with a pair of accurate and measurable features, more particularly parallel faces separated by a precise specified thickness, so as to provide for simple field calibration of opposite-facing DS sensors. Illustratively, a composite calibration object can be constructed, which includes the two-sided frustum that has been sandblasted and anodized (to provide measurable, repeatable features), with a flange whose above/below parallel surfaces have been ground to a precise specified thickness. The 3D corner positions of the two-sided frustum are used to calibrate the two sensors in X and Y, but cannot establish absolute Z without accurate information about the thickness of the two-sided frustum; the flange provides the absolute Z information.

A background scenery portion may be identified in each of a plurality of image sets of an object, where each image set includes images captured simultaneously from different cameras. A correspondence between the image sets may determined, where the correspondence tracks control points associated with the object and present in multiple images. A multi-view interactive digital media representation of the object that is navigable in one or more dimensions and that includes the image sets may be generated and stored.

Implementations are described herein for analyzing a sequence of digital images captured by a mobile vision sensor (e.g., integral with a robot), in conjunction with information (e.g., ground truth) known about movement of the vision sensor, to determine spatial dimensions of object(s) and/or an area captured in a field of view of the mobile vision sensor. Techniques avoid the use of visual indicia of known dimensions and/or other conventional tools for determining spatial dimensions, such as checkerboards. Instead, techniques described herein allow spatial dimensions to be determined using less resources, and are more scalable than conventional techniques.