An active sensor for performing active measurements of a scene is presented. The active sensor includes at least one transmitter configured to emit light pulses toward at least one target object in the scene, wherein the at least one target object is recognized in an image acquired by a passive sensor; at least one receiver configured to detect light pulses reflected from the at least one target object; a controller configured to control an energy level, a direction, and a timing of each light pulse emitted by the transmitter, wherein the controller is further configured to control at least the direction for detecting each of the reflected light pulses; and a distance measurement circuit configured to measure a distance to each of the at least one target object based on the emitted light pulses and the detected light pulses.

An image processing unit identifies the shape of an obstacle that is identified from an area that appears in a peripheral image based on an image captured by a camera. The shape of the obstacle includes at least a tilt of a section of the obstacle in a road-surface direction. The section of the obstacle faces a vehicle. The image processing unit generates a superimposed image in which a mark image that is generated as a pattern that indicates the identified obstacle is superimposed onto a position that corresponds to the obstacle in the peripheral image. At this time, the image processing unit variably changes properties of the mark image based on the tilt of the obstacle identified by an obstacle identifying unit. The image processing unit then displays the generated superimposed image on display apparatus.

Systems and methods are provided for generating a rear view image display for a motor vehicle. A rear view system includes an optical sensor disposed at the motor vehicle and configured to capture image data, a computational unit coupled to the optical sensor by a cable connection and configured to execute program instructions stored on a computer-readable medium to modify the image data for presentation, and a display device coupled to the computational unit and configured to receive the modified image data from the computational unit and display the modified image data to a driver of the motor vehicle. The computational unit is further configured to receive software calibration to optimize modification of the image data.

An information processing apparatus according to one embodiment includes a memory having computer executable components stored therein; and processing circuitry communicatively coupled to the memory. The processing circuitry is configured to: acquire, for each of a plurality of sensors installed in a vehicle, positional information of an object present around the vehicle measured by the sensor; calculate a probability that the object is present for each of a plurality of areas obtained by dividing surroundings of the vehicle based on the positional information measured by the sensors; record non-measurement information indicating that the positional information was not obtained for an area corresponding to a direction in which the positional information was not obtained for each of the sensors; and determine a final probability that the object is present based on the probability calculated for each of the sensors and the non-measurement information.

A method for estimating time to collision (TTC) of a detected object in a computer vision system is provided that includes determining a three dimensional (3D) position of a camera in the computer vision system, determining a 3D position of the detected object based on a 2D position of the detected object in an image captured by the camera and an estimated ground plane corresponding to the image, computing a relative 3D position of the camera, a velocity of the relative 3D position, and an acceleration of the relative 3D position based on the 3D position of the camera and the 3D position of the detected object, wherein the relative 3D position of the camera is relative to the 3D position of the detected object, and computing the TTC of the detected object based on the relative 3D position, the velocity, and the acceleration.

This application provides a collision detection method and related apparatus. An image taken by a photographing unit may be used to predict whether a collision with a to-be-detected target will occur. In a current collision prediction method, a type of the to-be-detected target needs to be determined first based on the image taken by the photographing unit, which requires consuming of a large amount of computing power. In the collision prediction method provided in this application, a change trend of a distance between the to-be-detected target and a vehicle in which the apparatus is located may be determined based on the distances between the to-be-detected target and the vehicle at different moments, to predict a collision between the to-be-detected target and the vehicle. This method can improve efficiency in collision prediction and reduce energy consumption in predicting collision.

A relative position of an object in the surroundings of a vehicle is estimated based on a two-dimensional camera image. A control unit determines an object contour of the object from the camera image and determines at least one digital object template that represents the object based on the object contour. The control unit forward projects the at least one object template from respective different positions onto an image plane of the camera image. Each forward-projected object template yields a respective two-dimensional contour proposal, and the control unit compares the contour proposals with the object contour of the object.

A method and apparatus for recognizing an object are provided, including extracting a feature from an input image and generating a feature map in a neural network. In parallel with the generating of the feature map, a region of interest (ROI) corresponding to an object of interest is extracted from the input image, and a number of object candidate regions used to detect the object of interest is determined based on a size of the ROI. The object of interest is recognized from the ROI based on the number of object candidate regions in the neural network.

A method for generating a first person view (FPV) of an environment includes, with aid of one or more processors individually or collectively, analyzing stereoscopic video data of the environment to determine environmental information and generating augmented stereoscopic video data of the environment by fusing the stereoscopic video data and the environmental information.

An object detection device (1) includes: a correction unit (41) that corrects, by using projective transformation, distortion of an image (Im) captured by a camera (2) capturing an image of a detection target object and mounted on a construction machine (100); and a processing unit (42) that sets, in the image (Im) after correction, a plurality of regions (R-1, R-2, and R-3) provided in accordance with a distance between the camera (2) and the object and each having a different area, and performs image processing of detecting the object with respect to a part of the image (Im) corresponding to each of the plurality of regions (R-1, R-2, and R-3).