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: A system may be configured to provide natural language search over security videos. In some aspects, the system may generate a first representation of sampled video information in a multidimensional format via a first machine learning model, and receive a request including a natural language input. Further, the system may generate a second representation of the natural language input in the multidimensional format via a second machine learning model that is a different from the first machine learning model, and determine that the first representation has a predefined relationship with the second representation. In addition, the system may present the second representation as a response to the request based on the first representation having the predefined relationship with the second representation.

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: Examples relate to processing a first image captured at a first time to determine a first set of multiple key points of a face in the first image, processing a second image captured at a second time to determine a second set of the multiple key points on the face in the second image, determining a location of at least a portion of the multiple key points in a three-dimensional space based on a first location of each of the portion of the multiple key points in the first image and a second location of each of the portion of the multiple key points in the second image, and detecting whether the face is a valid three-dimensional face for facial recognition based at least in part on the location of at least the portion of the multiple key points in the three-dimensional space.

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: Examples relate to processing a first image captured by a camera at a first time to determine a first set of multiple key points of a face in the first image, processing a second image captured by the camera at a second time to determine a second set of the multiple key points on the face in the second image, determining a location of at least a portion of the multiple key points in a three-dimensional space based on a first location of each of the portion of the multiple key points in the first image and a second location of each of the portion of the multiple key points in the second image, and detecting whether the face is a valid three-dimensional face for facial recognition based at least in part on the location of at least the portion of the multiple key points in the three-dimensional space.

Abstract: Example implementations include a method, apparatus and computer-readable medium for detecting an object in an image, by applying, on a first input image, a first object detector configured to generate a bounding box around the object in the first input image. The implementations further include identifying a first bounding box having a first bounding box size around the object in the first input image and determining that the first bounding box size is less than a threshold size. Additionally, the implementations further include generating a second input image by cropping an area of the original image corresponding to the first bounding box and identifying, by applying a second object detector on the second input image, a second bounding box around the object in the second input image. Additionally, the implementations further include performing an image analysis function on the object using information from the second bounding box.

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: Examples relate to processing a first image captured by a camera at a first time to determine a first set of multiple key points of a face in the first image, processing a second image captured by the camera at a second time to determine a second set of the multiple key points on the face in the second image, determining a location of at least a portion of the multiple key points in a three-dimensional space based on a first location of each of the portion of the multiple key points in the first image and a second location of each of the portion of the multiple key points in the second image, and detecting whether the face is a valid three-dimensional face for facial recognition based at least in part on the location of at least the portion of the multiple key points in the three-dimensional space.

Abstract: Examples described herein generally relate to processing a first image captured by the first camera at a first time to determine a first set of multiple key points of a face in the first image, processing a second image captured by the second camera at a second time, that is equal to or within a threshold time of the first time, to determine a second set of the multiple key points on the face in the second image, determining a location of at least a portion of the multiple key points in a three-dimensional space based on a first location of each of the portion of the multiple key points in the first image and a second location of each of the portion of the multiple key points in the second image, and detecting whether the face is a valid three-dimensional face for facial recognition based at least in part on the location of at least the portion of the multiple key points in the three-dimensional space.

Abstract: Aspects of the present disclosure include methods, systems, and non-transitory computer readable media that perform the steps of receiving a first plurality of snapshots, generating a first plurality of descriptors each associated with the first plurality of snapshots, grouping the first plurality of snapshots into at least one cluster based on the plurality of descriptors, selecting a representative snapshot for each of the at least one cluster, generating at least one second descriptor for the representative snapshot for each of the at least one cluster, wherein the at least one second descriptor is more complex than the first plurality of descriptors, and identifying a target by applying the at least second descriptor to a second plurality of snapshots.

Abstract: Examples described herein generally relate to processing a first image captured by the first camera at a first time to determine a first set of multiple key points of a face in the first image, processing a second image captured by the second camera at a second time, that is equal to or within a threshold time of the first time, to determine a second set of the multiple key points on the face in the second image, determining a location of at least a portion of the multiple key points in a three-dimensional space based on a first location of each of the portion of the multiple key points in the first image and a second location of each of the portion of the multiple key points in the second image, and detecting whether the face is a valid three-dimensional face for facial recognition based at least in part on the location of at least the portion of the multiple key points in the three-dimensional space.

Abstract: Aspects of the present disclosure include methods, systems, and non-transitory computer readable media that perform the steps of receiving a first plurality of snapshots, generating a first plurality of descriptors each associated with the first plurality of snapshots, grouping the first plurality of snapshots into at least one cluster based on the plurality of descriptors, selecting a representative snapshot for each of the at least one cluster, generating at least one second descriptor for the representative snapshot for each of the at least one cluster, wherein the at least one second descriptor is more complex than the first plurality of descriptors, and identifying a target by applying the at least second descriptor to a second plurality of snapshots.

Abstract: Examples described herein generally relate to processing a first image captured by the first camera at a first time to determine a first set of multiple key points of a face, processing a second image captured by the second camera at a second time to determine a second set of the multiple key points on the face in the second image, determining a location of at least a portion of the multiple key points in a three-dimensional space based on a first location of each of the portion of the multiple key points in the first image and a second location of each of the portion of the multiple key points in the second image, and detecting whether the face is a valid three-dimensional face for facial recognition based at least in part on the location of at least the portion of the multiple key points in the three-dimensional space.

Abstract: The present disclosure relates to unsupervised enrollment for an anti-theft facial recognition system. Specifically, a security system may generate a unique set of facial recognition biometric features for a human face of a person of interest based on analyzing one or more face images in a track, extracting one or more potential facial recognition biometric features, and removing outlier ones of the one or more potential facial recognition biometric features to define the unique set of facial recognition biometric features. The security system may further determine whether a new face detected in a new video includes a new set of facial recognition biometric features that is a match with one of the plurality of unique sets of facial recognition biometric features corresponding to the plurality of known human faces of a plurality of persons of interest, and generate an alert in response to determining the match.

Abstract: Aspects of the present disclosure include methods, systems, and non-transitory computer readable media that perform the steps of receiving one or more snapshots, extracting one or more features from the one or more snapshots, and providing the one or more features to a first classification layer for classifying a first target and a second classification layer for re-identifying a second target.

Abstract: Aspects of the present disclosure include methods, systems, and non-transitory computer readable media that perform the steps of receiving a first plurality of snapshots, generating a first plurality of descriptors each associated with the first plurality of snapshots, grouping the first plurality of snapshots into at least one cluster based on the plurality of descriptors, selecting a representative snapshot for each of the at least one cluster, generating at least one second descriptor for the representative snapshot for each of the at least one cluster, wherein the at least one second descriptor is more complex than the first plurality of descriptors, and identifying a target by applying the at least second descriptor to a second plurality of snapshots.