Abstract: Methods for performing improving facial recognition of a driver in a vehicle are disclosed. A set of images is received. Each of the set of images includes a portion that is identified as a face. The identification is based on an application of a first machine-learned model to each of the set of images. The application of the first machine-learned model is performed by an application associated with a client camera device mounted in a vehicle. Based on a determination that the set of images matches one or more reference images stored in a database with a confidence level that is equal to or greater than a confidence threshold, a person corresponding to the one or more reference images is associated as a driver of the vehicle during a time period in which the set of images was captured.

Abstract: A vehicle dash cam may be configured to execute one or more neural networks (and/or other artificial intelligence), such as based on input from one or more of the cameras and/or other sensors associated with the dash cam, to intelligently detect safety events in real-time. Detection of a safety event may trigger an in-cab alert to make the driver aware of the safety risk. The dash cam may include logic for determining which asset data to transmit to a backend server in response to detection of a safety event, as well as which asset data to transmit to the backend server in response to analysis of sensor data that did not trigger a safety event. The asset data transmitted to the backend server may be further analyzed to determine if further alerts should be provided to the driver and/or to a safety manager.

Abstract: A video gateway device at a worksite (or other location) is coupled to multiple cameras on a network, such as multiple cameras at a business site. The gateway device determines video stream capabilities of each camera and, based on the capabilities, determines whether any additional video steams need to be created for particular cameras. For example, for cameras that provide only a high-resolution video stream, the gateway will generate (e.g., via downsampling) a standard-resolution video stream for artificial intelligence (“AI”) analysis and a low-resolution video stream for viewing by a user. However, for cameras that provide both a high and standard-resolution video stream, the gateway device uses the camera-provided standard-resolution video stream for AI analysis, rather than generating a new video stream on the gateway device.

Abstract: A vehicle dash cam may be configured to execute one or more neural networks (and/or other artificial intelligence), such as based on input from one or more of the cameras and/or other sensors associated with the dash cam, to intelligently detect safety events in real-time. Detection of a safety event may trigger an in-cab alert to make the driver aware of the safety risk. The dash cam may include logic for determining which asset data to transmit to a backend server in response to detection of a safety event, as well as which asset data to transmit to the backend server in response to analysis of sensor data that did not trigger a safety event. The asset data transmitted to the backend server may be further analyzed to determine if further alerts should be provided to the driver and/or to a safety manager.

Abstract: Methods for performing improving facial recognition of a driver in a vehicle are disclosed. A set of images is received. Each of the set of images includes a portion that is identified as a face. The identification is based on an application of a first machine-learned model to each of the set of images. The application of the first machine-learned model is performed by an application associated with a client camera device mounted in a vehicle. Based on a determination that the set of images matches one or more reference images stored in a database with a confidence level that is equal to or greater than a confidence threshold, a person corresponding to the one or more reference images is associated as a driver of the vehicle during a time period in which the set of images was captured.

Abstract: An Internet of Things (IoT) or vehicle dash cam may store both a high-resolution and low-resolution video stream on a device. The video streams are selectively accessible by remote devices. Because of the relatively smaller storage requirements of low-resolution video files, retaining of additional video data on the vehicle device (beyond what would be possible with only high-resolution video) is possible. The user may be provided an option to adjust the amount of low-resolution and high-resolution video to store on the device. A combined media file may be generated by a device to include time-synced high-resolution video, low-resolution video, and/or metadata for a particular time period.

Abstract: Methods for performing improving facial recognition of a driver in a vehicle are disclosed. A set of images is received. Each of the set of images includes a portion that is identified as a face. The identification is based on an application of a first machine-learned model to each of the set of images. The application of the first machine-learned model is performed by an application associated with a client camera device mounted in a vehicle. Based on a determination that the set of images matches one or more reference images stored in a database with a confidence level that is equal to or greater than a confidence threshold, a person corresponding to the one or more reference images is associated as a driver of the vehicle during a time period in which the set of images was captured.