Abstract: A method for generating and selecting images of objects based on video data and text data includes receiving, at a processor of a video camera system, a video stream including a series of video frames depicting at least one object. A set of at least one classification for the object is generated. Additionally, an image that depicts the object and that includes a cropped portion of a video frame from the series of video frames is generated. A set of at least one index key is generated based on the set of at least one classification, and the image is stored based on the set of at least one index key. The processor receives a signal representing a text input from a user, and the processor performs at least one of (1) retrieval of the image or (2) generation of an alert.

Abstract: A trajectory-based object search process includes receiving a user request specifying a geographic region of interest and/or a time period of interest. A set of video-derived object trajectories is identified based on the user request. At least one event is identified based on the set of video-derived object trajectories, by processing each video-derived object trajectory individually or by aggregating video-derived object trajectories, from the set of video-derived object trajectories, that overlap with respect to at least one feature. For each event, a preview image is selected that includes a thumbnail image and/or a video frame, based on the user request and using a ranking algorithm, and event metadata associated with the preview image is received. Also for each event, a video segment associated with that event is received, and the preview image and/or the video segment is caused to be displayed to a user associated with the user request.

Abstract: A non-transitory, processor-readable medium stores instructions that, when executed by a processor, cause the processor to generate, using a first compute device, a video file. The non-transitory, processor-readable medium also stores instructions that, when executed by a processor, cause the processor to cause the video file to be sent to a second compute device that is cloud based, to cause the second compute device to: encode the video file, using a set of at least one software container, to generate an encoded video file; execute, at the set of at least one software container, a streaming protocol to generate a stream based on the encoded video file; and cause, via the set of at least one software container, generation of a set of analytics associated with the video file, based on the stream and using a virtualized software application environment.

Abstract: First video data captured by a first camera from a set of cameras is received and caused to be stored in a first data stripe from a set of data stripes. Second video data captured by the first camera is received and caused to be stored in a second data stripe from the set of data stripes. An indication is received that the first video data has become inaccessible. Data associated with the first data stripe is received from a subset of cameras from the set of cameras that excludes the first camera. The first video data is generated based on the data received from the subset of cameras.

Abstract: A computer vision processor of a camera generates hyperzooms for persons or vehicles from image frames captured by the camera. The hyperzooms include a first hyperzoom associated with the persons or vehicles. The computer vision processor tracks traffic patterns of the persons or vehicles while obviating network usage by the camera by predicting positions of the persons or vehicles using a Kalman Filter from the first hyperzoom. The persons or vehicles are detected in the second hyperzoom. The positions of the persons or vehicles are updated based on detecting the persons or vehicles in the second hyperzoom. The first hyperzoom is removed from the camera. Tracks of the persons or vehicles are generated based on the updated positions. The second hyperzoom is removed from the camera. Track metadata is generated from the tracks for storing in a key-value database located on a non-transitory computer-readable storage medium of the camera.

Abstract: A first set of values reported by an electronic device and not reported by another electronic device over a first period of time that is prior to a firmware release to the electronic device is received. The first set of values is associated with a metric. A set of statistical properties associated with the first set of values is determined. A second set of values reported by the electronic device and not reported by another electronic device over a second period of time that is after the firmware release is received. The second set of values is associated with the metric. A set of statistical properties associated with the second set of values is determined. The set of statistical properties associated with the first set of values and the set of statistical properties associated with the second set of values is compared to detect an anomaly.

Abstract: A method includes building a firmware image to execute on a bootloader of a system on chip (SoC), the firmware image including first encryption public and private keys, and digitally signing the firmware image with a second encryption private key. The signed firmware image is encrypted with a symmetric encryption key, which in turn is encrypted with a second encryption public key. The encrypted signed firmware image and the encrypted symmetric encryption key are sent to the SoC to cause the SoC to (1) decrypt the encrypted symmetric encryption key to produce the symmetric encryption key using a third encryption private key from a first asymmetric key pair, (2) decrypt the encrypted signed firmware image to produce the signed firmware image using the symmetric encryption key, and (3) verify a digital signature of the signed firmware image using a third encryption public key from a second asymmetric key pair.

Abstract: A first set of values reported by an electronic device and not reported by another electronic device over a first period of time that is prior to a firmware release to the electronic device is received. The first set of values is associated with a metric. A set of statistical properties associated with the first set of values is determined. A second set of values reported by the electronic device and not reported by another electronic device over a second period of time that is after the firmware release is received. The second set of values is associated with the metric. A set of statistical properties associated with the second set of values is determined. The set of statistical properties associated with the first set of values and the set of statistical properties associated with the second set of values is compared to detect an anomaly.

Abstract: A first set of values reported by an electronic device and not reported by another electronic device over a first period of time that is prior to a firmware release to the electronic device is received. The first set of values is associated with a metric. A set of statistical properties associated with the first set of values is determined. A second set of values reported by the electronic device and not reported by another electronic device over a second period of time that is after the firmware release is received. The second set of values is associated with the metric. A set of statistical properties associated with the second set of values is determined. The set of statistical properties associated with the first set of values and the set of statistical properties associated with the second set of values is compared to detect an anomaly.

Abstract: Introduced here are surveillance systems and associated approaches for obfuscating biometric identifiers generated by, or available to, those surveillance systems for privacy reasons. In the event that a surveillance system generates a biometric identifier (e.g., by imaging a face of a person entering the premises of an organization), caution must be taken to only share the biometric identifier with individuals as necessary for security reasons. The approaches introduced here allow the privacy of persons being surveilled to be protected. Specifically, a computer program may selectively apply blurring as an impermanent mask to obfuscate each face in a digital image. This approach limits “reidentification” of a given person to those scenarios where her identity must be determined.

Abstract: A method for performing detection-based object searches includes receiving a user request indicating a region of interest, a timeframe of interest, or an object of interest. A signal is sent to cause execution of a query to identify object detections based on the user request. A signal representing at least one event identified in response to the query is received. For each event from the at least one event, a thumbnail image is identified based on the user request and using a ranking algorithm. A video frame identified based on the thumbnail image is received, and a video segment associated with the video frame is retrieved, A preview image clip that includes the video frame and the video segment is generated and displayed to a user associated with the user request.

Abstract: A computer vision processor of a camera extracts attributes of persons or vehicles from hyperzooms generated from image frames. The hyperzooms represent traffic patterns. The extracting is performed using a feature extractor of an on-camera convolutional neural network (CNN) including an inverted residual structure. The attributes include at least colors of clothing of the persons or colors of the vehicles. Mobile semantic segmentation models of the CNN are generated using the hyperzooms and the attributes. Attribute analytics are generated by executing the mobile semantic segmentation models while obviating network usage by the camera. The attribute analytics are stored in a key-value database located on a memory card of the camera. A query is received from the server instance specifying one or more of the attributes. The attribute analytics are filtered using the one or more of the attributes to obtain a portion of the traffic patterns.

Abstract: A method for generating and selecting images of objects based on video data and text data includes receiving, at a processor of a video camera system, a video stream including a series of video frames depicting at least one object. A set of at least one classification for the object is generated. Additionally, an image that depicts the object and that includes a cropped portion of a video frame from the series of video frames is generated. A set of at least one index key is generated based on the set of at least one classification, and the image is stored based on the set of at least one index key. The processor receives a signal representing a text input from a user, and the processor performs at least one of (1) retrieval of the image or (2) generation of an alert.

Abstract: First video data captured by a first camera from a set of cameras is received and caused to be stored in a first data stripe from a set of data stripes. Second video data captured by the first camera is received and caused to be stored in a second data stripe from the set of data stripes. An indication is received that the first video data has become inaccessible. Data associated with the first data stripe is received from a subset of cameras from the set of cameras that excludes the first camera. The first video data is generated based on the data received from the subset of cameras.

Abstract: A method for monitoring protected regions of a camera view includes receiving, at a processor of a video camera system, a video stream including a series of video frames depicting at least one scene. A background model is identified based on a predefined representation of at least one region of interest associated with the scene. Each of the video frames is compared to the background model, to define an associated binary mask. A persistent change in the at least one region of interest is detected, based on the binary masks, the persistent change having an associated duration that exceeds a predefined threshold. In response to detecting the persistent change, a change notification message including a video file is generated. The method also includes causing display of the change notification message via a user interface and/or causing transmission of the change notification message to a remote compute device.

Abstract: A method for generating cropped images depicting objects captured in video data includes receiving, at a processor of a video camera system, a video stream including a series of video frames depicting an object. A classification for the object is generated, and an occurrence of the object being detected is identified in an additional video frame(s) from the series of video frames. A motion associated with the object is calculated based on the classification and the additional video frame(s). At least one image that depicts the object and includes a cropped portion of a video frame from the series of video frames is generated, along with an associated set of at least one quality score, in response to calculating the motion. The method also includes causing transmission of the at least one image to at least one remote compute device based on the set of at least one quality score.

Abstract: A first document including a decrypting version of a first key and a second document including a representation of a login token are received from the first compute device. An encrypted second key that has been encrypted by an encrypting version of the first key is received after receiving the login token from a second compute device. The second compute device stores the encrypting version of the first key before the receiving of the first document. The encrypted second key is decrypted using the decrypting version of the first key to obtain a plaintext second key. Encrypted sensor data that includes plaintext sensor data that has been (1) captured prior to the receiving of the first document, and (2) encrypted by the plaintext second key is received from the second compute device. The encrypted sensor data is decrypted using the plaintext second key to obtain the plaintext sensor data.

Abstract: A method includes obtaining a plurality of representative vectors associated with face-related data. The method includes determining an encryption key based on a parameter stored in a record, generating an encrypted vector set by, for each respective vector of the plurality of representative vectors, encrypting the respective vector with a homomorphic encryption operation based on the encryption key, where the encrypted vector set includes a first encrypted vector that is linked to a subset of the face-related data associated with the first plurality of face vectors. The method further includes obtaining an encrypted face search vector using the encryption key to perform homomorphic encryption. The method further includes selecting a first encrypted vector based on the encrypted face search vector and retrieving the subset of the face-related data based on the first encrypted vector.

Abstract: A non-transitory, processor-readable medium stores instructions that, when executed by a processor, cause the processor to generate, using a first compute device, a video file. The non-transitory, processor-readable medium also stores instructions that, when executed by a processor, cause the processor to cause the video file to be sent to a second compute device that is cloud based, to cause the second compute device to: encode the video file, using a set of at least one software container, to generate an encoded video file; execute, at the set of at least one software container, a streaming protocol to generate a stream based on the encoded video file; and cause, via the set of at least one software container, generation of a set of analytics associated with the video file, based on the stream and using a virtualized software application environment.

Abstract: A method includes receiving, at a processor and from a requestor compute device, a request including a representation of at least one video camera from a set of video cameras. In response to receiving the request, a set of at least one video file is generated by retrieving a set of video clips associated with the at least one video camera and the time range. For each of the at least one video camera, a subset of video clips is selected from the set of video clips for that video camera based on a comparison of anomaly scores for that video camera, and concatenating the subsets of video clips to generate the set of at least one video file. The method also includes sending a signal to cause display of at least one video file from the set of at least one video file via the requestor compute device.