The present disclosure provides a method and apparatus for detecting a traffic anomaly, a device, a storage medium and a computer program product, relates to the field of artificial intelligence, and specifically to computer vision and deep learning technologies, and can be applied to intelligent transportation scenarios. A specific implementation of the method comprises: acquiring a traffic video stream; performing vehicle detection tracking on the traffic video stream to determine whether there is an abnormally stopped vehicle, wherein a stop with a time length exceeding a preset time length belongs to an abnormal stop; and performing a traffic anomaly classification on a video frame corresponding to the abnormal stop using a decision tree to obtain a traffic anomaly type, if there is the abnormally stopped vehicle, wherein the decision tree is generated based on features for a traffic anomaly detection.

A multi-task deep learning-based real-time matting method for non-green-screen portraits is provided. The method includes: performing binary classification adjustment on an original dataset, inputting an image or video containing portrait information, and performing preprocessing; constructing a deep learning network for person detection, extracting image features by using a deep residual neural network, and obtaining a region of interest (ROI) of portrait foreground and a portrait trimap in the ROI through logistic regression; and constructing a portrait alpha mask matting deep learning network. An encoder sharing mechanism effectively accelerates a computing process of the network. An alpha mask prediction result of the portrait foreground is output in an end-to-end manner to implement portrait matting. In this method, green screens are not required during portrait matting. In addition, during the matting, only original images or videos need to be provided, without a need to provide manually annotated portrait trimaps.

A system for determining whether shoppers are eligible for frictionless checkout is disclosed. The system has a processor that obtains image data captured using image sensors positioned in a retail store. The processor analyzes the image data to identify at least one shopper at one or more locations of the retail store. The processor detects, based on the analysis of the image data, at least one product interaction event associated with an action of the at least one shopper at the one or more locations of the retail store. Further, based on the detected at least one product interaction event, the processor determines whether the at least one shopper is eligible for frictionless checkout. In response to a determination that the at least one shopper is ineligible for frictionless checkout, the processor causes delivery of an indicator that the at least one shopper is ineligible for frictionless checkout.

According the present invention, there is provided a control system (10) that includes an image acquisition unit (11) that acquires an image generated by a camera, a controlled object determination unit (12) that analyzes the image and determines at least one of a vehicle that satisfies a predetermined condition and a vehicle in which a predetermined person is riding included in the image, as a vehicle to be controlled, a control content decision unit (13) that decides a control content for the vehicle to be controlled, and an output unit (14) that outputs a control command including the control content to the vehicle to be controlled.

The present disclosure relates to a method performed by a people distinguishing system (1) for in an image distinguishing human beings in a crowd. The people distinguishing system identifies (1001) one or more detected objects classified as human beings (2) in an image (3) derived from a thermal camera (10) adapted to capture a scene in an essentially forward-looking angle. The people distinguishing system further identifies at least a first grouping (4) of adjoining pixels in the image, not comprised in the one or more detected human beings, having an intensity within a predeterminable intensity range. Moreover, the people distinguishing system determines (1003) a grouping pixel area (40) of the at least first grouping in the image. Furthermore, the people distinguishing system determines (1004) for at least a first vertical position (y.sub.expected) in the image, based on head size reference data (5), an expected pixel area (x.sub.expected) of a human head at the at least first vertical position. The people distinguishing system further compares (1005) at least a portion of the grouping pixel area with the expected head pixel area for the at least first vertical position. Moreover, the people distinguishing system determines (1006) that the at least first grouping comprises at least a first overlapping human being (6), when at least a first comparison resulting from the comparing exceeds a predeterminable conformity threshold. The disclosure also relates to a people distinguishing system in accordance with the foregoing, a thermal camera comprising such people distinguishing system, and a respective corresponding computer program product and non-volatile computer readable storage medium.

A method trains a neural network to identify an event based on discrepancies in answers to factoid questions at different times. One or more processors identify answers to a series of factoid questions. The processor(s) compare the answers from the series of factoid questions in order to determine discrepancies in the answers at different times, and then train a neural network to identify an event based on the discrepancies in the answers at the different times.

Determining occupants' interactions in a space by applying a computer vision algorithm to track an occupant in a set of images of a space to obtain locations in the space of the occupant over time, where a history log of the occupant includes the locations of the occupant in the space over time is created and history logs of a plurality of occupants are compared to extract interaction points between the plurality of occupants.

To improve calculation efficiency by properly reducing the number of regression regions based on a size of an object to be detected, a processing apparatus has at least one processor or circuit configured to function as: a size obtain unit configured to obtain a size of a predetermined object in the image; a set unit configured to divide the image into a plurality of regions based on the size of the predetermined object obtained by the size obtain unit, and to set regression regions for estimating a number of the predetermined object, wherein the set unit is configured to inhibit to set the regression regions smaller than a predetermined minimum size corresponding to the predetermined object, and an estimate unit configured to estimate the number of the predetermined object by performing a regression process on the regression regions set by the set unit.

The present disclosure relates to a system and a processing method in association with the system for customizing audio experience. Customization of audio experience can be based on derivation of at least one customized audio response characteristic which can be applied to an audio device used by a person. The customized audio response characteristic(s) can be unique to the person.

A system and a method are described for monitoring a medical care environment. In one or more implementations, a method includes identifying a first subset of pixels within a field of view of a camera as representing a bed. The method also includes identifying a second subset of pixels within the field of view of the camera as representing an object (e.g., a subject, such as a patient, medical personnel; bed; chair; patient tray; medical equipment; etc.) proximal to the bed. The method also includes determining an orientation of the object within the bed.