Abstract: A system and method for improving the ability of a camera to detect objects or events occurring within its field of view and to accurately categorize them using Artificial Intelligence (AI) aided by input from users. The camera may include rules for determining when an object has entered its field of view, and for determining what category of object it is. When a new object is detected, an alert may be sent to a user and optionally to an analytics service as well. The user may provide input confirming whether the category of the event was correctly determined, and the analytics service may apply an AI algorithm to determine what, if any, changes should be made to the rule criteria in the camera. Updated rule criteria may be sent back to the camera thus improving its ability to detect objects in the future.

Abstract: A lamp socket assembly to be installed in a light socket and receive electrical power therefrom. The lamp socket assembly to provide electrical power to a surveillance device that detects an occurrence of a security event and notifies the lamp socket assembly of the occurrence. In response to the notification, the lamp socket assembly to turn on a light bulb.

Abstract: A method performed by an image capture device. The method includes determining whether to send a notification or determining whether to open a door based at least in part on at least one image captured by the image capture device.

Abstract: A lamp socket assembly to be installed in a light socket and receive electrical power therefrom. The lamp socket assembly to provide electrical power to a surveillance device that detects an occurrence of a security event and notifies the lamp socket assembly of the occurrence. In response to the notification, the lamp socket assembly to turn on a light bulb.

Abstract: A method and an apparatus for detecting an abnormal situation are disclosed. The method includes detecting whether a first target exists in an obtained image; recognizing whether the first target holds an object, when the first target exists in the image; obtaining motion information of the object, when the first target holds the object; and determining, based on the motion information of the object, whether the abnormal situation exists.

Abstract: Image data may be downloaded or otherwise obtained. An application executing on the computing device (or at least in communication with the computing device) can analyze the image data to recognize objects represented in the image data as well as determine locations or regions of the image that include the representations. Candidate regions of interest (e.g., bounding boxes) that include at least one detected object can be generated. A single region—a representative region—can be determined from the candidate regions based on user and/or application-defined criterion. The criteria may help ensure that the representative region includes statistical properties that at least satisfy threshold property levels, such as being a consistent estimator of the candidate regions. Notions of “average” or “median” regions can be applied to generate the representative region.

Abstract: Disclosed is an object dangerousness recognition method comprising steps of generating, based on an image captured by a stereo camera, a heterogeneous point cloud of an object in the image, each point in the heterogeneous point cloud having depth information and planar image information; determining, based on the depth information and the planar image information of each point in the heterogeneous point cloud, a solid shape of the object, and then, generating a first dangerousness parameter according to the solid shape; determining, based on the depth information and the planar image information of each point in the heterogeneous point cloud, a surface feature of the object, and then, generating a second dangerousness parameter according to the surface feature; and generating, based on the first and second dangerousness parameters, a comprehensive dangerousness parameter of the object.

Abstract: A method and an apparatus for detecting an abnormal situation are disclosed. The method includes detecting whether a first target exists in an obtained image; recognizing whether the first target holds an object, when the first target exists in the image; obtaining motion information of the object, when the first target holds the object; and determining, based on the motion information of the object, whether the abnormal situation exists.

Abstract: Disclosed is an object dangerousness recognition method comprising steps of generating, based on an image captured by a stereo camera, a heterogeneous point cloud of an object in the image, each point in the heterogeneous point cloud having depth information and planar image information; determining, based on the depth information and the planar image information of each point in the heterogeneous point cloud, a solid shape of the object, and then, generating a first dangerousness parameter according to the solid shape; determining, based on the depth information and the planar image information of each point in the heterogeneous point cloud, a surface feature of the object, and then, generating a second dangerousness parameter according to the surface feature; and generating, based on the first and second dangerousness parameters, a comprehensive dangerousness parameter of the object.