HANWHA VISION CO., LTD.

An object recognition model training method in a computing device is disclosed. In the present disclosure, an object of interest, which is an object for object recognition, is designated, and an object of non-interest excluding the object of interest is generated and used as learning data for the object recognition model. In the process of training the object recognition model, when an erroneously detected object occurs, the object recognition model may be retrained by automatically converting the erroneously detected object to the object of non-interest without feedback of the erroneous detection to the user. Accordingly, user convenience for processing the erroneously detected object is improved, which increases reliability of the object recognition model. This disclosure can be associated with artificial intelligence modules, drones (unmanned aerial vehicles (UAVs)), robots, augmented reality (AR) devices, virtual reality (VR) devices, devices related to 5G service, etc.

A surveillance camera and a control method for the surveillance camera are disclosed. The present disclosure makes it possible to determine whether at least one of a bounding box for a head region of an objected detected from a side view image and a bounding box for an entire body region thereof intersects a preset counting line, thereby counting people passing through the counting line. In the present disclosure, one or more of a surveillance camera, an autonomous vehicle, a user terminal, and a server may be linked to an artificial intelligence module, a robot, an augmented reality (AR) device, a virtual reality (VT) device, and a 5G service.

A surveillance camera and a control method for the surveillance camera are disclosed. The present disclosure makes it possible to determine whether at least one of a bounding box for a head region of an objected detected from a side view image and a bounding box for an entire body region thereof intersects a preset counting line, thereby counting people passing through the counting line. In the present disclosure, one or more of a surveillance camera, an autonomous vehicle, a user terminal, and a server may be linked to an artificial intelligence module, a robot, an augmented reality (AR) device, a virtual reality (VT) device, and a 5G service.

A method for updating a neural network model for object re-identification includes: storing a neural network model pre-trained for object re-identification; acquiring images from a surveillance camera device; detecting objects from the images and, obtaining training data from among the objects to update the neural network model according to a predetermined criterion; and inputting the training data to the neural network model in a feedforward manner to obtain image characteristic parameters corresponding to the training data, and updating the neural network model by reflecting the image characteristic parameters in the neural network model.

Disclosed is a method and apparatus for performing, by a management platform operatively connected to both a video surveillance platform and a positioning platform via network connectivity, at least one among image processing and video analytics on obtained images or video related to a detected asset tag to provide user awareness related to a location or movement of the asset tag in a defined environment.

There is provided a control method of an object information integrated monitoring system. The control method includes: recognizing at least one first object in a first image captured by a first camera, setting a bounding box with respect to the at least one first object, and tracking the at least one first object; obtaining analytical information by analyzing the first image including the at least one first object; and displaying, based on the analytical information, an image map corresponding to the captured first image, wherein the displaying comprises displaying, on the image map, analytical information of the at least one first object while tracking a movement path of the at least one first object on the image map.

Disclosed is a image processing apparatus and a method for controlling the image processing apparatus. The image processing apparatus according to an embodiment of the present disclosure may identify an object from an acquired image, determine whether the object is hidden by another object by using an aspect ratio of a bounding box of the detected object, and based on the object being hidden, estimate an entire length of the object based on coordinate information of the bounding box. Accordingly, the size information of the hidden object may be efficiently identified while a large amount of database is applied or resources of the apparatus is minimized. The present disclosure may be in connection with a surveillance camera, an automotive driving vehicle, an artificial intelligence module of at least one of a user terminal or a server, a robot, an augmented reality (AR) device, a virtual reality (VR) device, a device related to a 5G service, and the like.

Disclosed is a image processing apparatus and a method for controlling the image processing apparatus. The image processing apparatus according to an embodiment of the present disclosure may identify an object from an acquired image, determine whether the object is hidden by another object by using an aspect ratio of a bounding box of the detected object, and based on the object being hidden, estimate an entire length of the object based on coordinate information of the bounding box. Accordingly, the size information of the hidden object may be efficiently identified while a large amount of database is applied or resources of the apparatus is minimized. The present disclosure may be in connection with a surveillance camera, an automotive driving vehicle, an artificial intelligence module of at least one of a user terminal or a server, a robot, an augmented reality (AR) device, a virtual reality (VR) device, a device related to a 5G service, and the like.

A device and a method for image search in a surveillance camera system are disclosed. An image search device according to an embodiment of the present specification comprises a database for storing images captured from a plurality of cameras spaced apart from each other and metadata of the images, wherein, when a probe image is input, the device restricts a search range for searching for the same person as a person of interest in the probe image according to a predetermined criterion, performs primary ranking on an image within the restricted search range on the basis of feature vector information of the image, and then performs re-ranking according to an additional image similarity on the basis of a result of the primary ranking. Accordingly, the accuracy of an image search can be improved while reducing the amount of computation required for the image search. In the present specification, one or more among a surveillance camera, an autonomous vehicle, a user terminal, and a server may be linked to an artificial intelligence module, a robot, an augmented reality (AR) device, a virtual reality (VR) device, a device related to 5G services, and the like.

Provided are an apparatus and a method for image analysis for obtaining an absolute distance of each pixel for an entirety of a depth map. The apparatus for image analysis includes: an imaging unit configured to generate a captured image; an object detection unit configured to detect at least one object from the captured image; a depth map generation unit configured to generate a depth map for the captured image; and a map analysis unit configured to obtain an absolute distance of each pixel for an entire area of the depth map by selecting a standard object among the detected at least one object and referring to an absolute distance obtained for a standard map object corresponding to the standard object among map objects included in the depth map.