Abstract: A leak detection system and method or an internal cabin of a vehicle include a sensing device configured to detect one or both of water flow or water pressure within a water delivery conduit that is configured to deliver water to one or more water-drawing components within one or more areas of the internal cabin. The sensing device is configured to detect one or both of the water flow or the water pressure into the one or more water-drawing components. A control unit is in communication with the sensing device. The control unit is configured to monitor one or both of the water flow or the water pressure as detected by the sensing device, and determine a presence of a leak in relation to the one or more water-drawing components based on one or both of the water flow or the water pressure.

Abstract: A device, method and system for pruning video analytics parameters is provided. A device analyzes images from a camera installed at an installation location using a full initial machine learning model trained to detect objects in the images using video analytics parameters, the full initial machine learning model being independent of the installation location. The device gathers, for a period of time, statistics for: frequency of use of the video analytics parameters and/or error generation frequency in detecting the objects in the images. After the period of time, based on the statistics, the device determines that given video analytics parameters meets one or more pruning conditions; and, in response, prunes the given video analytics parameters such that the full initial machine learning model, initially provided for the camera, changes to an updated machine learning model for the camera.

Abstract: A device, method and system for installing video analytics parameters at a video analytics engine is provided. An example device determines that a classification of a first camera is one or more of similar to, and same as, a respective classification of at least one second camera. The example device retrieves, from a memory, video analytics parameters associated with the at least one second camera, the video analytics parameters stored at the memory in association with the respective classification. The example device causes installing of the video analytics parameters at a video analytics engine associated with the first camera.

Abstract: A device, method and system for pruning video analytics parameters is provided. A device analyzes images from a camera installed at an installation location using a full initial machine learning model trained to detect objects in the images using video analytics parameters, the full initial machine learning model being independent of the installation location. The device gathers, for a period of time, statistics for: frequency of use of the video analytics parameters and/or error generation frequency in detecting the objects in the images. After the period of time, based on the statistics, the device determines that given video analytics parameters meets one or more pruning conditions; and, in response, prunes the given video analytics parameters such that the full initial machine learning model, initially provided for the camera, changes to an updated machine learning model for the camera.

Abstract: A device, method and system for installing video analytics parameters at a video analytics engine is provided. An example device determines that a classification of a first camera is one or more of similar to, and same as, a respective classification of at least one second camera. The example device retrieves, from a memory, video analytics parameters associated with the at least one second camera, the video analytics parameters stored at the memory in association with the respective classification. The example device causes installing of the video analytics parameters at a video analytics engine associated with the first camera.