A device may receive accelerometer data and video data for a vehicle and may identify bounding boxes and object classes for objects near the vehicle. The device may identify tracks for the objects and may filter out tracks that are not associated with vehicles or vulnerable road users to generate one or more tracks or an indication of no tracks. The device may generate a collision cone identifying a drivable area of the vehicle to identify objects more likely to be involved in a collision and may filter out tracks from the one or more tracks, based on the bounding boxes, and to generate a subset of tracks or another indication of no tracks. The device may determine scores for the subset of tracks and may identify a track of the subset of tracks with a highest score. The device may perform actions based on the identified track.

Aspects relate to generating a contagion prevention health assessment. An exemplary apparatus includes an optical device, at least a processor communicatively connected to the optical device and a memory communicatively connected to the at least a processor and to the optical device; the processor configured to receive an authentication datum from a user, authenticate the user as a function of the authentication datum and scan the user as a function of the authentication, where scanning the user further includes using a motion recognition machine learning model, generate a guidance datum as a function of the scan and to receive at least a user datum. and generate a health assessment as a function of a contagion status machine learning model, where the contagion status machine learning model is configured to receive the guidance datum and the at least a user datum as input and output the health assessment.

The present disclosure provides techniques for optical inspection systems and methods for moving objects. In some embodiments, an optical inspection system includes: an image capturing device configured to acquire images from moving objects; a first-stage storage system; a second-stage processor; a second-stage storage system; a third-stage processor; and a third-stage storage system. In some embodiments, an optical inspection system, includes: an image capturing device; a volatile memory system; a first and a second second-stage processor; and a third-stage storage system. The second-stage processor or the first second-stage processor can be configured to analyze the images from the image capturing device. The third-stage processor or the second second-stage processor can be configured to process information from a processor and/or storage system and produce a report.

An image display method includes displaying, on a display surface, a first image based on first image data from a first input source; and establishing a correspondence between a feature of a first marker and the first input source when it is detected that the first marker is placed in a detection area included in the display surface while the first image is displayed on the display surface.

Described are approaches for assigning tasks between machine resources (e.g., AI task performers, AI task validators), human resources (e.g., task performers, task validators), and/or other smart systems to facilitate collaborative text detection, text recognition, and text retrieval in order to optimize system performance along a variety of different selection criteria specifying various performant dimensions, including, but not limited to improving system efficiency, reducing task performer and/or task validator idle time, improving triage outcomes, reducing data processing loads, maintaining client confidentiality, etc., that may be associated with one or more customers.

Embodiments of this application disclose a method for determining camera pose information of a camera of a mobile terminal. The method includes: obtaining a first image, a second image, and a template image, the first image being a previous frame of image of the second image, the first image and the second image being images including a respective instance of the template image captured by the mobile terminal using the camera at a corresponding spatial position; determining a first homography between the template image and the second image; determining a second homography between the first image and the second image; and performing complementary filtering processing on the first homography and the second homography, to obtain camera pose information of the camera, wherein the camera pose information of the camera represents a spatial position of the mobile terminal when the mobile terminal captures the second image using the camera.

A method and system of handling an alternative pick-up using vehicle information is disclosed. Imaging data is received from at least one imaging device. The at least one imaging device is configured to provide a field-of-view of a predetermined area associated with a retail location. A vehicle identifier for each vehicle within the image data is extracted and compared to each user profile associated with an order in a set of orders. Each user profile includes a primary vehicle identifier and at least one secondary vehicle identifier and the extracted vehicle identifier is compared to the at least one secondary vehicle identifier in each user profile. The extracted vehicle identifier is associated with a first order in the set of orders when the extracted vehicle identifier matches the secondary vehicle identifier of the user profile of the first order.

A system and method for calibrating a machine vision system on the undercarriage of a rail vehicle while the rail vehicle is in the field is presented. The system enables operators to calibrate the machine vision system without having to remove the machine vision system from the undercarriage of the rail vehicle. The system can capture, by a camera of an image capturing module, a first image of a target. The image capturing module and a drum can be attached to a fixture and the target can be attached to the drum. The system can also determine a number of lateral pixels in a lateral pitch distance of the image of the target, determining a lateral object pixel size based on the number of lateral pixels, and determining a drum encoder rate based on the lateral object pixel size. The drum encoder rate can be programmed into a drum encoder.

Presented herein are embodiments of a vision-based object perception system for activity analysis, safety monitoring, or both. Embodiments of the perception subsystem detect multi-class objects (e.g., construction machines and humans) in real-time while estimating the poses and actions of the detected objects. Safety monitoring embodiments and object activity analysis embodiments may be based on the perception result. To evaluate the performance of embodiments, a dataset was collected including multi-class of objects in different lighting conditions with human annotations. Experimental results show that the proposed action recognition approach outperforms the state-of-the-art approaches on top-1 accuracy by about 5.18%.

Described are approaches for assigning tasks between machine resources (e.g., AI task performers, AI task validators), human resources (e.g., task performers, task validators), and/or other smart systems to facilitate collaborative text detection, text recognition, and text retrieval in order to optimize system performance along a variety of different selection criteria specifying various performant dimensions, including, but not limited to improving system efficiency, reducing task performer and/or task validator idle time, improving triage outcomes, reducing data processing loads, maintaining client confidentiality, etc., that may be associated with one or more customers.