Abstract: Device configurations for a machine-learned model. A device analytics system includes an electronic processor configured to determine, for a machine-learned model configured to detect a plurality of features, a prioritization ranking for a plurality of input parameters provided to the machine-learned model and receive, for each device, a confidence value for each feature included in the plurality of features. The electronic processor is configured to determine, for each device included in the plurality of devices, a performance value for each feature included in the plurality of features based on the prioritization ranking for the plurality of input parameters and the confidence value for each feature for the respective device. The electronic processor is configured to select, based on the performance value for each device included in the plurality of devices for a feature, a device configuration for the machine-learned model, and implement the selected device configuration.

Abstract: Device configurations for a machine-learned model. A device analytics system includes an electronic processor configured to determine, for a machine-learned model configured to detect a plurality of features, a prioritization ranking for a plurality of input parameters provided to the machine-learned model and receive, for each device, a confidence value for each feature included in the plurality of features. The electronic processor is configured to determine, for each device included in the plurality of devices, a performance value for each feature included in the plurality of features based on the prioritization ranking for the plurality of input parameters and the confidence value for each feature for the respective device. The electronic processor is configured to select, based on the performance value for each device included in the plurality of devices for a feature, a device configuration for the machine-learned model, and implement the selected device configuration.

Abstract: Method and 3D modeling server (110) to generate a 3D model. The method includes receiving first images captured by a camera (320) corresponding to an incident scene and receiving first metadata generated by a time-of-flight sensor (325) corresponding to the first images. The method also includes generating a 3D model at a first resolution including a plurality of 3D points based on the first images and the first metadata and identifying a first incident-specific point of interest from the first images. The method further includes transmitting for recapturing the first incident-specific point of interest and receiving second images captured of the first incident-specific point of interest. The method also includes receiving second metadata generated corresponding to the second images and updating a first portion of the 3D model corresponding to the first incident-specific point of interest to a second resolution based on the second images and the second metadata.

Abstract: A process of requesting and providing an anonymization service for sharing images or videos capturing identity of persons. An anonymization server receives a request including information corresponding to an identifier identifying the image or the video, a region within the image or video in which an identity of a person is captured, an encryption key used to encrypt the region of the image or the video, and contact information of the person. When the anonymization server receives a request from the second agency to share an encryption key for decrypting the region, the server transmits a request to the person to provide a permission to deanonymize the identity of the person captured in the image or video. The anonymization server transmits a response including the encryption key to the second agency when a response indicating a permission is received from the person.