Cortica Ltd.

Autobrains Technologies Ltd.

Roi Saida

Igal Raichelgauz

Shay Ieizerovitch

A method for lane boundary detection. The method may include obtaining an image of an environment of a vehicle. The environment may include at least one lane boundary; the images may include image segments; calculating image segment lane boundary metadata for relevant image segments. The calculating is executed by a neural network and is based on the image segments; wherein each relevant image segment comprises one or more lane boundary points candidates; wherein image segment lane boundary metadata that is related to a relevant image segment which may include (i) location information about location of the one or more lane boundary points candidates of the relevant image segment, and (ii) angular information regarding an angle between lane boundary points candidates of the image; and determining an estimate of a lane boundary of the at least one lane boundary; wherein the determining is based on the image segment lane boundary metadata of the relevant image segments; wherein the determining comprises applying a process that differs from neural network processing.

Lane boundary detection

A method for driving-related object detection, the method may include receiving an input image by an input of an object detector; and detecting, by an object detector, objects that appear in the input image. The detecting includes searching for (i) a first object having a first size that is within a first size range and belongs to a four wheel vehicle class, (ii) a second object having a second size that is within a second size range and belongs to a subclass out of multiple four wheel vehicle subclasses, (iii) a pedestrian, and (iv) a two wheel vehicle; wherein a maximum of the first size range does not substantially exceed a minimum of the second size range.

Method for training a driving related object detector

Object detection based on shallow neural network that processes input images

Systems, and method and computer readable media that store instructions for calculating signatures, utilizing signatures and the like.

Low-power calculation of a signature of a media unit

A method that may include feeding an input image and downscaled versions of the input image to multiple branches of an object detector calculating, by the multiple branches, candidate bounding boxes; and selecting bounding boxes. The multiple branches comprise multiple shallow neural networks that are followed by multiple region units. Each branch includes a shallow neural network and a region unit. The multiple shallow neural networks are multiple instances of a single trained shallow neural network. The single trained shallow neural network is trained to detect objects having a size that is within a predefined size range and to ignore objects having a size that is outside the predefined size range.

Growing community of inventors

Acco, Israel

Roi Saida