Abstract: Example implementations include a method, apparatus, and computer-readable medium for object detection, comprising detecting a first object in a first image frame and a second image frame, wherein the first object is bounded by region-of-interest (ROI) boundaries generated by an ROI detection model. The implementations further include calculating a speed of the first object using positions of the first object in the first and second image frame, identifying at least one image frame that should include the first object based on a calculated speed of the first object. The implementations further include determining that the at least one image frame should be added to a training dataset for the ROI detection model in response to detecting that the ROI detection model did not generate a ROI boundary in the at least one image frame, and subsequently re-training the ROI detection model using said training dataset.

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: 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.

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: A visual feedback access control device and methods for use thereof. The visual feedback access control device may comprise a camera configured to obtain an image. The visual feedback access control device may also comprise a plurality of light emitting diodes (“LEDs”) arranged in an plurality of segments relative to the camera. The visual feedback access control device may also comprise where the plurality segments being individually controllable. The visual feedback access control device may also comprise having the LEDs configured to illuminate upon a received control signal based upon one or more access control trigger events.

Abstract: The invention provides controllers systems and methods that apply techniques for determining media stream characteristics that propagate through a sequence of buffers. One of the buffers represents buffering characteristics of a network through which the media stream propagates. The media stream may include packets of a media stream and the network may include a wireless network.

Abstract: The invention provides controllers systems and methods that apply fuzzy logic techniques for determining signal sequence characteristics that propagate through a sequence of buffers. One of the buffers represents buffering characteristics of a network through which the signal sequence propagates. The signal sequence may include packets of a media stream and the network may include a wireless network.