A computer device extracts a plurality of target windows from a target video. Each of the target windows comprises a respective plurality of consecutive video frames. For each of the target windows, the device performs action recognition on the respective plurality of consecutive video frames corresponding to the target window to obtain respective first action feature information of the target window. The device obtains a similarity between the first action feature information of the target window and preset feature information. The device determines, from the respective obtained similarities corresponding to the plurality of target windows, a highest first similarity and a first target window corresponding to the highest first similarity. The device also determines a dynamic action corresponding to the highest first similarity as the preset dynamic action in accordance with threshold settings.

A method is presented. The method includes determining a number of landmarks in an image comprising multiple pixels. The method also includes determining a number of channels for the image based on a function of the number of landmarks. The method further includes determining, for each one of the number of channels, a confidence of each pixel of the multiple pixels corresponding to a landmark. The method still further includes identifying the landmark in the image based on the confidence.

Embodiments herein provide a method and system for real time ROI detection in thermal face images based on a heuristic approach. The ROI of the thermal images, once detected, is then further used to detect temperature of a subject corresponding to the ROI. Unlike state of the art techniques, the heuristic approach is computationally less intensive and provides fast and accurate ROI detection even in case of occluded faces in a crowd with a single thermal image having a plurality of subject being scanned. The heuristics applied does not focus on face detection but directly on point of interest detection. Once the point of interest (ROI) is detected, it may be used for plurality of applications such as subject tracking and the like, not limited to subject or object temperature sensing since the method disclosed herein is easily implementable on low power devices.

Disclosed is a method in which trigger information is obtained. The trigger information is associated with at least part of attribute information associated with at least one first consignment unit. It is determined whether or not attribute information associated with at least one second consignment unit is stored in a database that is similar to the attribute information associated with the at least one first consignment unit. If the attribute information associated with at least one second consignment unit is determined to be stored in the database that is determined to be similar to the attribute information associated with the at least one first consignment unit, then alert information is generated.

An image providing apparatus (10) includes a registration unit (12) for registering a plurality of captured images of a predetermined user taken at a plurality of locations and captured times of the plurality of captured images, respectively, in a storage unit (11) in association with each other, a specification unit (13) for specifying two or more location captured images including a face area whose degree of match with a face area of the user included in a specific image captured at a specific location is greater than or equal to a predetermined value from among the plurality of captured images, and an output unit (15) for outputting the two or more specified location captured images in order of the captured times.

A method and device for image processing, a terminal and a storage medium. The method includes acquiring an image generated by an infrared sighting telescope arranged on a firearm; determining a grey level dynamic range of a preset first portion of the image; if a difference value between the grey level dynamic range value and the grey level of the preset second portion reaches a preset starting processing threshold, performing mean filtering processing on each pixel of the preset second portion to obtain a filtered grey level; and if the filtered grey level is greater than a preset grey level expectation threshold, processing the pixel, so that the grey level of the pixel is close to the grey level expectation value.

A processor acquires a plurality of first training data in which area information indicating an area in which each of a plurality of regions is present is added to a first training image which is at least a part of a plurality of training images each including the plurality of regions, and a plurality of second training data in which relationship information indicating a relationship between the plurality of regions is added to a second training image which is at least a part of the plurality of training images. The processor calculates, for each first training image, a first evaluation value for training an estimation model such that the plurality of regions specified by using the estimation model match the area information. The processor derives, for each second training image, estimation information in which the relationship indicated by the relationship information is estimated by using the estimation model to calculate a second evaluation value indicating a degree of deviation between the estimation information and the relationship information. The processor trains the estimation model such that a loss including, as elements, the first evaluation value and the second evaluation value is reduced.

A plurality of training data in which range information indicating a range in which a first region and a second region including at least a part of the first region are present is added to each of a plurality of training images each including the first region and the second region are acquired. For each pixel of the training image, a probability that the pixel is a portion of the first region that is not included in the second region is calculated by using an estimation model. A probability sum, which is a sum of the probabilities, is calculated for each of the plurality of training images. The estimation model is trained such that the probability sum calculated for each of training images in which the first region has the portion that is not included in the second region is increased and the probability sum calculated for each of training images in which the first region does not have the portion that is not included in the second region is zero.

A system and method of inspecting a static monitoring installation, installed in a traffic space. An evaluation circuit is able to create an image of the environment from a signal reflected from an object, wherein at least one reference value of a reference image of the environment is stored in the evaluation circuit, and the at least one reference value is formed from the reflected signals of at least one reference point for a reflected signal

A system and method of inspecting a static monitoring installation, installed in a traffic space. An evaluation circuit is able to create an image of the environment from a signal reflected from an object, wherein at least one reference value of a reference image of the environment is stored in the evaluation circuit, and the at least one reference value is formed from the reflected signals of at least one reference point for a reflected signal