Abstract: 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.

Abstract: 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.

Abstract: A method for determining driving related features of a vehicle, which may include repeating, during each time interval out of multiple time intervals of a period: obtaining sensed information during at least a part of the time interval; selecting, based on a predefined decision, a selected sub-set of narrow artificial intelligence (AI) agents; and calculating one or more driving related feature. The calculating may include applying the selected sub-set of narrow AI agents on the sensed information.

Abstract: A method for determining driving related features of a vehicle, the method may include: repeating, during each time interval out of multiple time intervals of a period; obtaining sensed information during at least a part of the time interval; receiving context information determined before the time interval; selecting, based on the context information, a selected sub-set of narrow artificial intelligence (AI) agents or calculating one or more driving related features. The calculating may include applying the selected sub-set of narrow AI agents on the sensed information; and determining, based on the sensed information, context information to used following the time interval.

Abstract: An apparatus that may include a neural network processor, the neural network processor comprises multiple building blocks. Each of the at least some of the building blocks may include, may consist or may consist essentially of an input, an output and at least one array convolution unit.

Abstract: An apparatus that may include a neural network processor, the neural network processor comprises multiple building blocks. Each of the at least some of the building blocks may include, may consist or may consist essentially of an input, an output and at least one array convolution unit.

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

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method that may include receiving or generating multiple versions of an input image, wherein the multiple versions differ from each other by resolution; feeding the multiple versions of the input image to multiple branches of an object detector; wherein the multiple branches comprise multiple shallow neural networks that are followed by multiple region units; calculating, by the multiple branches, candidate bounding boxes that are indicative of candidate objects that appear in the multiple versions of the input image; wherein the calculating comprises feeding intermediate results from shallow neural networks of lower resolution branches to shallow neural networks of higher resolution branches; and selecting bounding boxes out of the candidate bounding boxes, by a selection unit that follows the multiple branches

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

Abstract: 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.