Abstract: Example implementations include a method, apparatus and computer-readable medium for estimating a real world height in an image, comprising obtaining two images of an object, with known height, wherein each image depicts the object at a different position that is estimated using a homography matrix. The homography matrix transforms an image plane associated with the image to a real world plane. The implementations include generating a first vector that intersects with the object and points to a real world ground plane using the first image and a second vector that intersects with the same point on the object and points to the real world ground plane using the second image. Additionally, the implementations further include calibrating the camera by determining a height of the center of a camera lens of the camera using an intersection of the first vector and the second vector.

Abstract: Example implementations include a method, apparatus and computer-readable medium for estimating a real world distance in an image, comprising obtaining an image from a camera installed in an environment and identifying, in the image, a first set of parallel lines on a real world ground plane and a second set of parallel lines on the real world ground plane. The implementations include determining a first intersection point of the first set of parallel lines and a second intersection point of the second set of parallel lines, identifying a ground line connecting the first intersection point and the second intersection point, and estimating an internal camera matrix based on a focal length and an image size associated with the camera. The implementations include calibrating the camera for real world distance measurement by determining a perpendicular vector to the ground line based on the internal camera matrix.

Abstract: Example implementations include a method, apparatus and computer-readable medium for estimating a real world height in an image, comprising obtaining a first image and a second image from a camera installed at an unknown height in an environment, wherein the first image includes a first object of known height at a first location and the second image includes the first object at a second location; determining, based on the known object height, a first vector that intersects with a point on the first object in the first image and points to a real world ground plane; determining, based on the known object height, a second vector that intersects with the point on the first object in the second image and points to the real world ground plane; and determining a height of the center of a camera lens of the camera using an intersection of the first and second vector.

Abstract: A security device maintenance system is provided. The aspects include one or more processors configured to assess security data quality by conducting one or more tests that compare the security data to one or more predetermined thresholds, assign one or more anomaly indicators to security devices if the security data quality fails to meet the one or more predetermined thresholds, and generate an automated maintenance report for the one or more security devices including the one or more anomaly indicators. The aspects can also include periodically retrieving one or more security data from the one or more security devices, utilizing an artificial intelligence (AI) model to detect one or more anomalies, and generating one or more predictive maintenance suggestions.

Abstract: Systems and methods are disclosed for indicating occupancy of a point of interest in a room. A video feed from a camera located in the room can be received, and a person in the video feed can be detected by using an artificial intelligence (AI) model. A location of the person on a floor plan of the room can be identified based on a video feed location of the person detected in the video feed, and where the location of the person on the floor plan is at the point of interest for at least a threshold period of time, the point of interest can be indicated as being occupied.

Abstract: A video camera recording system is provided. The aspects include one or more memories configured to store program code. The program code is for performing an automated verification test of elements of the video camera recording system using at least one artificial intelligence (AI) model to identify potential issues with the elements of the video camera recording system. The aspects further include one or more processors, operatively coupled to the one or more memories and configured to run the program code. The aspects also include a transceiver configured to transmit instructions causing a corrective action to be performed for at least one of the potential issues.

Abstract: Example implementations include a method, apparatus and computer-readable medium for estimating a real world distance in an image, comprising obtaining an image from a camera installed in an environment and identifying, in the image, a first set of parallel lines on a real world ground plane and a second set of parallel lines on the real world ground plane. The implementations include determining a first intersection point of the first set of parallel lines and a second intersection point of the second set of parallel lines, identifying a ground line connecting the first intersection point and the second intersection point, and estimating an internal camera matrix based on a focal length and an image size associated with the camera. The implementations include calibrating the camera for real world distance measurement by determining a perpendicular vector to the ground line based on the internal camera matrix.

Abstract: Example implementations include a method, apparatus and computer-readable medium for estimating a real world height in an image, comprising obtaining two images of an object, with known height, wherein each image depicts the object at a different position that is estimated using a homography matrix. The homography matrix transforms an image plane associated with the image to a real world plane. The implementations include generating a first vector that intersects with the object and points to a real world ground plane using the first image and a second vector that intersects with the same point on the object and points to the real world ground plane using the second image. Additionally, the implementations further include calibrating the camera by determining a height of the center of a camera lens of the camera using an intersection of the first vector and the second vector.

Abstract: A method determines an identification value of a rotatable element having a plurality of mirror facets. The identification value is determined based on a facet scan duration obtained of the mirror facets or based on a prismatic error of the mirror facets.

Abstract: A method determines an identification value of a rotatable element having a plurality of mirror facets. The identification value is determined based on a facet scan duration obtained of the mirror facets or based on a prismatic error of the mirror facets.