Argo Ai Gmbh

Tel Aviv University

Asaf Kagan

Rotem Littman

Dana Berman

Gilad Saban

Noam Presman

Menachem Nathan

Amir Averbuch

Michael Golub

Valery Zheludev

Roman Malinsky

Guy Leibovitz

Matthew Lee Gilson

Disclosed herein are systems, methods, and computer program products for predicting movement of an object in a real-world environment. The methods comprise: obtaining a plurality of image frames captured in a sequence during a period of time; identifying first image frames of the plurality of image frames that contain an image of at least one object with one or more turn signals; analyzing the first image frames to obtain a classification for a pose of the at least one object; using the classification of the pose of the at least one object to further obtain a type classification for at least one of the turn signals and a state classification for a state of at least one of the turn signals; and predicting movement of the at least one object based at least on the type and state classifications obtained for at least one of the turn signals.

Using captured video data to identify pose of a vehicle

A system uses data captured by vehicle-mounted sensors to generate a view of a ground surface. The system does this by receiving digital image frames and associating a location and pose of the vehicle that captured the image with each digital image frame. The system will access a three dimensional (3D) ground surface estimation model of the ground surface, select a region of interest (ROI) of the ground surface, and select a vehicle pose. The system will identify digital image frames that are associated with the pose and also with a location that corresponds to the ROI. The system will generate a visual representation of the ground surface in the ROI by projecting ground data for the ROI from the ground surface estimation model to normalized 2D images that are created from the digital image frames. The system will save the visual representation to a two-dimensional grid.

Ground surface imaging combining LiDAR and camera data

A system uses video of a vehicle to detect and classify the vehicle's pose. The system generates an image stack by scaling and shifting a set of digital image frames from the video to a fixed scale, yielding a sequence of images over a time period. The system processes the image stack with a classifier to determine the pose of the object. The system also may determine state and class of visible turn signals on the object, as well as predict the vehicle's direction of travel.

A system uses video of a vehicle or other object to detect and classify an active turn sign on the object. The system generates an image stack by scaling and shifting a set of digital image frames from the video to a fixed scale, yielding a sequence of images over a time period. The system processes the image stack with a classifier to determine a pose of the object, as well as the state and class of each visible turn signals on the object. When the system determines that a turn signal is active, the system will predict an action that the object will take based on the class of that signal.

Using captured video data to identify active turn signals on a vehicle

Snapshot spectral imagers comprise an imaging lens, a dispersed image sensor and a restricted isometry property (RIP) diffuser inserted in the optical path between the source image and the image sensor. The imagers are used to obtain a plurality of spectral images of the source object in different spectral bands in a single shot. In some embodiments, the RIP diffuser is one dimensional. An optional disperser may be added in the optical path, to provide further dispersion at the image sensor. In some embodiments, all imager components except the RIP diffuser may be part of a digital camera, with the RIP diffuser added externally. In some embodiments, the RIP diffuser may be included internally in a digital camera.

Growing community of inventors

Herzliya, Israel

Asaf Kagan