An image information overlay system retrieves an image associated with a vehicle listing and uses machine learning models to classify the image, generating identification data that may comprise a vehicle make and model, a feature or part of the vehicle present in the image, and a location of the vehicle feature or part. The identification data or an individual identifier of the vehicle, such as a Vehicle Identification Number (VIN), may be used to retrieve overlay information related to the vehicle make and model, such as recalls or known maintenance issues or information specific to the vehicle, such as mileage, accident reports, or ownership history. The overlay information is displayed on the image as an overlay at the location of the vehicle feature or part corresponding to the overlay information.

An image classification system and method is used to detect the parking status of lightweight vehicles, such as kick scooters. The system and method uses a deep learning model to analyze and classify ambiguous parking states that are likely to be encountered by lightweight vehicles, which are small and light enough to be parked in many different environments.

A leak detection system is configured to provide vehicle-specific leak detection for a vehicle. The leak detection system includes a memory circuit having a plurality of leak detection instructions matched with vehicle identification information. The system additionally includes an ultraviolet light source and a camera configured to capture an image of an area illuminated by the ultraviolet light source. A processor is in operative communication with the memory circuit and the camera. The processor is configured to facilitate identification of at least one of the plurality of leak detection instructions in response to receipt of vehicle identification information associated with the vehicle, and compare the image captured by the camera to a known image of the vehicle to determine presence and location of a leak.

A method for finding at least one trigger for human intervention in a control of a vehicle, the method may include receiving, from a plurality of vehicles, and by an I/O module of a computerized system, visual information acquired during situations that are suspected as situations that require human intervention in the control of at least one of the plurality of vehicles; determining, based at least on the visual information, the at least one trigger for human intervention; and transmitting to one or more of the plurality of vehicles, the at least one trigger.

The disclosure relates to a method for warehouse storage-location monitoring, a computer device, and a storage medium. The method includes the following. Video data of a warehouse storage-location area is obtained, and a target image corresponding to the warehouse storage-location area is obtained based on the video data, where the warehouse storage-location area includes an area of a storage-location and an area around the storage-location. The target image is detected based on a category detection model, to determine a category of each object appearing in the target image, where the category includes at least one of: human, vehicle, or goods. A detection result is obtained by detecting a status of each object based on the category of each object, where the detection result includes at least one of: whether the human enters the warehouse storage-location area, vehicle status information, or storage-location inventory information. The detection result is transmitted to a warehouse scheduling system, where the detection result is used for the warehouse scheduling system to monitor the warehouse storage-location area.

In an approach to safely facilitate driver responses to road traffic event alerts, computer-implemented methods, computer program products, and computer systems for reducing vehicle occupant distractions are described. The computer-implemented method includes processors configured for receiving vehicle alert data corresponding to a road traffic event, generating a user alert prompt corresponding to the vehicle alert data, transmitting the user alert prompt to a user vehicle satisfying a first condition, and receiving a user response from an occupant of the user vehicle. Responsive to receiving an affirmative user response, activating one or more vehicle activity systems to reduce vehicle cabin activity by occupants within the user vehicle.

An information processing apparatus according to an embodiment of the present technology includes a generation unit. The generation unit generates, with respect to a target object in an image for learning on the basis of input information regarding an outer shape of the target object, which is input from a user, a three-dimensional region surrounding the target object as a label. Accordingly, the annotation accuracy can be improved.

Systems, methods, and software described herein manage the playback speed of video data based on processing objects in the video data. In one example, a video processing service obtains video data from a video source and identifies objects of interest in the video data. The video processing service further determines complexity in frames of the video data related to the objects of interest and updates playback speeds for segments of the video data based on the complexity of the frames.

A method of generating a context-rich parking event of a target vehicle taken by a patrol vehicle; including obtaining a plate read event identifying an identifier of the target vehicle; initiating a collection of a first context image of a first view of the target vehicle; obtaining of geolocation information; obtaining temporal information; verifying if at least one condition is met by calculating if at least one of: a temporal constraint threshold is reached by using the temporal information; and a position constraint threshold is reached by using the geolocation information; initiating a collection by the patrol vehicle of a second context image of a second view of the target vehicle; and causing an association between the second context image and the plate read event to generate the context-rich parking event.

The present application discloses a vehicle re-identification method and apparatus, a device and a storage medium, which relates to the field of computer vision, intelligent search, deep learning and intelligent transportation. The specific implementation scheme is: receiving a re-identification request from a terminal device, the re-identification request including a first image of a first vehicle shot by a first camera and information of the first camera; acquiring a first feature of the first vehicle and a first head orientation of the first vehicle according to the first image; determining a second image of the first vehicle from images of multiple vehicles according to the first feature, multiple second features extracted based on the images of the multiple vehicles in an image database, the first head orientation of the first vehicle, and the information of the first camera; and transmitting the second image to the terminal device.