A surveillance apparatus (100) includes a feature value storage apparatus (200) that associates and stores a feature value of a person belonging to the same group, a detection unit (102) that detects an approach of a person not belonging to the same group to the person belonging to the same group within a reference distance by processing a captured image by using the feature value, and an output unit (104) that performs a predetermined output by using a detection result of the detection unit (102).

Erroneous detection due to erroneous parallax measurement is suppressed to accurately detect a step present on a road. An in-vehicle environment recognition device 1 includes a processing device that processes a pair of images acquired by a stereo camera unit 100 mounted on a vehicle. The processing device includes a stereo matching unit 200 that measures a parallax of the pair of images and generates a parallax image, a step candidate extraction unit 300 that extracts a step candidate of a road on which the vehicle travels from the parallax image generated by the stereo matching unit 200, a line segment candidate extraction unit 400 that extracts a line segment candidate from the images acquired by the stereo camera unit 100, an analysis unit 500 that performs collation between the step candidate extracted by the step candidate extraction unit 300 and the line segment candidate extracted by the line segment candidate extraction unit 400 and analyzes validity of the step candidate based on the collation result and an inclination of the line segment candidate, and a three-dimensional object detection unit 600 that detects a step present on the road based on the analysis result of the analysis unit 500.

A delivery management server, connected to a sender terminal and a carrier terminal over a network, includes a registration means for writing destination information of a delivery object received from the sender terminal and a first image in which a random pattern on a surface of the delivery object is captured, into a storage means in association with each other; a matching means for performing matching between a second image in which a random pattern on a surface of the delivery object is captured, received from the carrier terminal, and the first image stored in the storage means; and a presentation means for displaying the destination information associated with the first image stored in the storage means on a terminal screen of the carrier terminal, on the basis of a result of the matching.

A delivery management server, connected to a sender terminal and a carrier terminal over a network, includes a registration means for writing destination information of a delivery object received from the sender terminal and a first image in which a random pattern on a surface of the delivery object is captured, into a storage means in association with each other; a matching means for performing matching between a second image in which a random pattern on a surface of the delivery object is captured, received from the carrier terminal, and the first image stored in the storage means; and a presentation means for displaying the destination information associated with the first image stored in the storage means on a terminal screen of the carrier terminal, on the basis of a result of the matching.

Methods and apparatuses for image processing are provided. A first image belonging to a first image domain is acquired and input to an image processing model to be trained to obtain a second image belonging to a second image domain. A first correlation degree between an image feature of the first image and an image feature of the second image to obtain a target feature correlation degree is calculated. A second correlation degree between feature value distribution of the image feature of the first image and feature value distribution of the image feature of the second image is calculated to obtain a distribution correlation degree. Model parameters of an image processing model are adjusted to a direction in which the target feature correlation degree is increased and a direction in which the distribution correlation degree is increased to obtain a trained image processing.

An image registration method includes: acquiring a target image comprising a target object; inputting the target image to a preset network model, and outputting position information and rotation angle information of the target object; obtaining a reference image comprising the target object by querying a preset image database according to the position information and the rotation angle information; and performing image registration on the target image and the reference image to obtain a corresponding position of the target object of the target image in the reference image.

In the field of video processing, a detection and tracking method and apparatus, and a storage medium, are provided. The method includes: performing feature point analysis on a video frame sequence, to obtain feature points on each video frame thereof; performing target detection on an extracted frame through a first thread based on the feature points, to obtain a target box in the extracted frame; performing target box tracking in a current frame through a second thread based on the feature points and the target box in the extracted frame, to obtain a result target box in the current frame; and outputting the result target box. As the target detection and the target tracking are divided into two threads, a tracking frame rate is unaffected by a detection algorithm, and the target box of the video frame can be outputted in real time, improving real-time performance and stability.

The present disclosure provides a method and apparatus for processing an image. A specific implementation includes: acquiring a top view of a road; identifying a position of a lane line from the top view; cutting the top view into at least two areas, and determining, according to the position of the lane line in each area, a width of a lane in the each area and an average width of the lane in the top view; calculating a first perspective correction matrix by optimizing a first loss function, the first loss function being used to represent a difference between the width of the lane in the each area and the average width of the lane in the top view; and performing a lateral correction on the top view through the first perspective correction matrix to obtain a first corrected image.

A point cloud data encoding method and decoding method, a device, a medium, and a program product are provided, and relate to the field of point cloud application technologies. One method includes obtaining point cloud data, the point cloud data comprising at least two data points; and sequentially encoding data points in the point cloud data according to encoding orders of the data points, to obtain encoded point cloud data corresponding to the point cloud data, wherein the encoding orders of the data points being determined based on distances among the data points. Another method includes obtaining encoded point cloud data, obtaining reference information, the reference information being used for indicating a start reference data point of an encoding queue; and sequentially decoding, based on the reference information and the encoded point cloud data, data points according to the encoding orders of the data points.

Embodiments of the disclosed systems and methods provide for determination of roadway traversability by an autonomous vehicle using real time data and a trained traversability determination machine learning model. Consistent with aspects of the disclosed embodiments, the model may be trained using annotated birds eye view perspective data obtained using vehicle vision sensor systems (e.g., LiDAR and/or camera systems). During operation of a vehicle, vision sensor data may be used to construct birds eye view perspective data, which may be provided to the trained model. The model may label and/or otherwise annotate the vision sensor data based on relationships identified in the model training process to identify associated road boundary and/or lane information. Local vehicle control systems may compute control actions and issue commands to associated vehicle control systems to ensure the vehicle travels within a desired path.