An identification module is disclosed for use in a key making machine. The identification module may have a key receiving assembly configured to receive only a shank of an existing key. The identification module may also have a tip guide, configured to receive a tip of the shank of the existing key. The tip guide may have a slot that exposes a tip end of the shank. The identification module may also have an imaging assembly configured to capture an image of the tip end through the slot.

Examples disclosed herein relate to a person moving in a physical space. In one aspect, a method is disclosed. The method may include obtaining at least two images of a person from at least two cameras directed at a physical space, where the physical space may include a plurality of designated areas. The method may also include obtaining metadata associated with the images, based on the images and the metadata determining within the plurality of designated areas a set of designated areas visited by the person, for each designated area within the set of designated areas, determining an area information, and updating a database based on the set of designated areas and based on at least a portion of the area information associated with each designated area within the set of designate areas.

Aspects of the present disclosure relate to eye tracking systems and methods which track eyes by illuminating the eyes using a light source and detecting the eye illuminations using a sensor. Implementations of the present disclosure may utilize wide angle lighting via a plurality of individual light sources which are each oriented in different orientations. A wide area may be illuminated by the different light sources, and these light sources may be selectively turned on and off based on a current location of a user.

A system for use in a vehicle for determining an indication of the type of terrain in the vicinity of the vehicle, the system comprising; means configured to receive sensor output data from at least one sensor on the vehicle; means configured to determine a plurality of parameters in dependence on the sensor output data; a neural network algorithm configured to receive the plurality of parameters; and means configured to execute the neural network algorithm to provide a plurality of outputs corresponding to a plurality of different terrain types, the neural network being further configured to associate the plurality of parameters with one of the plurality of outputs, so as to determine an indication of the terrain type.

The various implementations described herein include methods, devices, and systems for detecting motion and persons. In one aspect, a method is performed at a smart home system that includes a video camera, a server system, and a client device. The video camera captures video and audio, and wirelessly communicates, via the server system, the captured data to the client device. The server system: (1) receives and stores the captured data from the video camera; (2) determines whether an event has occurred, including detected motion; (3) in accordance with a determination that the event has occurred, identifies video and audio corresponding to the event; and (4) classifies the event. The client device receives information indicative of the identified events, displays a user interface for reviewing the video and audio stored by the remote server system, and displays the at least one classification for the event.

The invention presents a process for determining a video as being a spoof or a genuine recording of a live biometric characteristic, characterized in that it comprises the steps of: preprocessing (200) the video, determining a liveness score (300) of the video, said determination comprising, for each frame of a plurality of frames (j) of the video: computing a difference between a motion intensity of a current frame and that of each frame of a set of preceding frames to infer a differential motion intensity of the current frame, inferring (330), from the differential motion intensities of the plurality of frames, a motion intensity of the video, comparing (340) said motion intensity to a predetermined threshold, and assigning a liveness score to the video, and determining (400) whether the video is a genuine recording of a biometric characteristic or a spoof.

A method for the preparation of an obstacle map, wherein the obstacle map comprises cells, includes assigning each of the cells to segments of an environment of the vehicle, and assigning to each of the cells information as to whether the corresponding segment of the environment is occupied by an obstacle. The method also includes preparing an environment map that comprises the cells, and determining a threshold value specification, where the threshold value specification specifies different threshold values for the cells of the environment map. The threshold value specification is determined depending on a trajectory of the vehicle. An obstacle map is then determined on the basis of the environment map and the threshold value specification.

A method for a computing device to enhance video chatting Includes receiving a live video stream, processing a frame in the live video stream in real-time, and transmitting the frame to another computing device. Processing the frame in real-time includes detecting a face, an upper torso, or a gesture in the frame, and applying a visual effect to the frame. The method includes processing a next frame in the live video stream in real-time by repeating the enhancing, the detecting, and the applying.

A traffic light detection device includes: an image capture unit capturing an image of surroundings; a traffic light location estimation unit estimating a location of a traffic light around the vehicle and setting a traffic light search area in which the traffic light is estimated to be present; a traffic light detection unit detecting the traffic light by searching the traffic light search area on the image; and an obstruction estimation unit. When the obstruction estimation unit estimates that a continuous obstruction state where a view of the traffic light is continuously obstructed occurs in the traffic light search area, the traffic light location estimation unit selects the traffic light search area based on the continuous obstruction state.

Automated occupant protection in stationary vehicles, including: detecting that an occupant is inside a stationary vehicle and that an operator is not in the stationary vehicle; detecting, based on one or more observations of the occupant, that one or more risk conditions have been met; and sending an alert to the operator in response to the one or more risk conditions being satisfied.