In some implementations, there is provided a method. The method may include receiving data characterizing a plurality of digital video frames; detecting a plurality of features in each of the plurality of digital video frames; determining, from the detected features, a local scale change and a translational motion of one or more groups of features between at least a pair of the plurality of digital video frames; and calculating a likelihood of collision. Related apparatus, systems, techniques, and articles are also described.

The present invention provides a method and apparatus for selectively enhancing regions in an image. In one embodiment, a digital image is read from an image source and is converted into a desired image model. One or more regions in the image having intensity values of pixels falling outside a pre-determined optimal intensity range are determined. The one or more regions in the image are then enhanced using a modeled light source of an optimal intensity such that the intensity value of pixels corresponding to the one or more regions in the image fall within the pre-determined optimal intensity range.

Disclosed are methods, systems, and non-transitory computer-readable medium for managing a vehicle. For instance, the method may include transmitting a notification to a first device. The method may further include receiving, from the first device, an image depicting an area, identifying a landing zone in the area, and performing an analysis by processing the image and determining whether an object is detected within a predetermined radius of a center point of the landing zone. The method may further include, in response to determining a presence of a non-removable object within the predetermined radius of the center point of the landing zone, transmitting instructions to the first device to obtain a new landing zone.

A method for classifying a behavior, of a pedestrian when crossing a roadway of a vehicle, includes reading in a sensor signal to detect the pedestrian and at least one piece of surroundings information regarding surroundings of the pedestrian. The sensor signal represents here a signal of at least one sensor of the vehicle. The method further includes ascertaining at least one physical variable of a correlation between the pedestrian and the at least one piece of surroundings information. Finally, the method includes classifying the behavior of the pedestrian using the at least one physical variable.

Systems and related methods for controlling vehicle position and movement are disclosed. A system includes one or more computers configured to receive data corresponding to video image frames of at least a portion of a course, and analyze the video image frame to estimate at least one of position and orientation of the vehicle within the course. A method includes identifying groups of pixels from the video image frames that are determined to correspond to non-background objects in the course. The method also includes correlating at least a portion of the identified groups of pixels with known objects in the course, and analyzing positions of the groups of pixels within the video image frames and known positions of at least one of an image capture device and known objects relative to the course to estimate at least one of the position and orientation of the vehicle within the course.

The present invention discloses a system and a method for optimizing Unmanned Aerial Vehicle (UAV) based warehouse management, where an optimized path for UAV is generated in real time based on the density of inventory. In operation, the present invention provides for identifying landmark features of the warehouse and density of inventory. Further, a 3D grid map an aisle of the warehouse is generated using the density of inventory. Finally, a navigation path for the UAV for a mission is generated based on the generated 3D grid map using one or more path planning techniques. Further, the present invention provides for updating the navigation path if one or more changes are observed in the density of the inventory.

An object detection method includes extracting an object candidate by grouping a plurality of characteristic points from two images acquired at different times by a camera; calculating, for each of characteristic points included in the object candidate, an observation flow amount indicating an amount of positional shift on the two images and a virtual road surface flow amount indicating an amount of positional shift on the two images while assuming that the characteristic point is located on a road surface; generating, for each of the characteristic points, a flow difference distribution of the object candidate by calculating a difference between the observation flow amount and the virtual road surface flow amount; and determining whether the object candidate is an object existing on and protruding from the road surface by comparing the flow difference distribution with a plurality of flow difference distribution models generated for predicted inclined states of the surface.

A method for determining the roadway plane in the surrounding area of a vehicle, the vehicle comprising a stereo camera system for capturing stereo images of the surrounding area of the vehicle and an artificial neural network for processing the image information provided by the stereo camera system, wherein the neural network determines disparity information of the surrounding area of the vehicle, wherein, on the basis of the disparity information, distance information is calculated which contains information regarding the distance of the objects displayed on the image information from the stereo camera system or the vehicle, wherein roadway distance information is extracted from the distance information, wherein the roadway plane is determined on the basis of the roadway distance information.

A method for tracking and/or characterizing multiple objects in a sequence of images. The method includes: assigning a neural network to each object to be tracked; providing a memory that is shared by all neural networks; providing a local memory for each neural network, respectively; supplying images from the sequence, and/or details of these images, to each neural network; during the processing of each image and/or image detail by one of the neural networks, generating an address vector from at least one processing product of this neural network; based on this address vector, writing at least one further processing product of the neural network into the shared memory and/or into the local memory, and/or reading out data from this shared memory and/or local memory and further processing the data by the neural network.

A fact checking system utilizes social networking information and analyzes and determines the factual accuracy of information and/or characterizes the information by comparing the information with source information. The social networking fact checking system automatically monitors information, processes the information, fact checks the information and/or provides a status of the information, including automatically modifying a web page to include the fact check results. The fact checking system is able to be implemented utilizing a drone device.