Abstract: A method implemented by a system for security comprising an unmanned aerial vehicle (UAV) and an analytics device configured to communicate with a remote monitoring system and the UAV is provided. Media data from the UAV is received at an analytics device, where the media data includes surveillance information corresponding to a premises under surveillance. Security attributes associated with the premises are detected based at least in part on a first level of machine learning (ML) analysis performed on the media data. The media data are transmitted by the analytics device to the remote monitoring system for a second level of ML analysis based at least in part on the security attribute, where the first level of ML analysis is less computationally expensive compared to the second level of ML analysis, and the second level of ML analysis is performed at the remote monitoring system on the media data.

Abstract: A computing system is described. The computing system is configured to receive, from a portal device of a premises monitoring system, a first request for a list of compatible pipelines for a premises analytics device of the premises monitoring system, identify a plurality of pipelines that are compatible with the premises analytics device based on a parameter, and transmit the list of compatible pipelines to the portal device. The computing system is further configured to receive, from the portal device, a second request to deploy a user selected pipeline of the plurality of pipelines referenced in the list of compatible pipelines, transmit a command, to a third-party system, to obtain a pipeline deployment based on the user selected pipeline, and receive, from the third-party system, the pipeline deployment based on the user selected pipeline. The pipeline deployment is transmitted to the premises analytics device.

Abstract: A control device in a premises security system for a premises is provided. The control device is configured to receive video surveillance data associated with an area of the premises, identify, using at least one machine learning model, a triggering event based at least in part on the video surveillance data, identify, using the at least one machine learning model, and based at least in part on the video surveillance data, a person associated with the triggering event, identify, using the at least one machine learning model, at least one characteristic of the person associated with the triggering event, generate an audio message comprising content based at least in part on the at least one characteristic of the person associated with the triggering event, and cause playback of the audio message in the area of the premises.

Abstract: A computing system is described. The computing system is configured to receive, from a portal device of a premises monitoring system, a first request for a list of compatible pipelines for a premises analytics device of the premises monitoring system, identify a plurality of pipelines that are compatible with the premises analytics device based on a parameter, and transmit the list of compatible pipelines to the portal device. The computing system is further configured to receive, from the portal device, a second request to deploy a user selected pipeline of the plurality of pipelines referenced in the list of compatible pipelines, transmit a command, to a third-party system, to obtain a pipeline deployment based on the user selected pipeline, and receive, from the third-party system, the pipeline deployment based on the user selected pipeline. The pipeline deployment is transmitted to the premises analytics device.

Abstract: A method implemented by a system for security comprising an unmanned aerial vehicle (UAV) and an analytics device configured to communicate with a remote monitoring system and the UAV is provided. Media data from the UAV is received at an analytics device, where the media data includes surveillance information corresponding to a premises under surveillance. Security attributes associated with the premises are detected based at least in part on a first level of machine learning (ML) analysis performed on the media data. The media data are transmitted by the analytics device to the remote monitoring system for a second level of ML analysis based at least in part on the security attribute, where the first level of ML analysis is less computationally expensive compared to the second level of ML analysis, and the second level of ML analysis is performed at the remote monitoring system on the media data.

Abstract: A computing system is described. The computing system is configured to receive, from a portal device of a premises monitoring system, a first request for a list of compatible pipelines for a premises analytics device of the premises monitoring system, identify a plurality of pipelines that are compatible with the premises analytics device based on a parameter, and transmit the list of compatible pipelines to the portal device. The computing system is further configured to receive, from the portal device, a second request to deploy a user selected pipeline of the plurality of pipelines referenced in the list of compatible pipelines, transmit a command, to a third-party system, to obtain a pipeline deployment based on the user selected pipeline, and receive, from the third-party system, the pipeline deployment based on the user selected pipeline. The pipeline deployment is transmitted to the premises analytics device.

Abstract: A control device in a premises security system for a premises is provided. The control device is configured to receive video surveillance data associated with an area of the premises, identify, using at least one machine learning model, a triggering event based at least in part on the video surveillance data, identify, using the at least one machine learning model, and based at least in part on the video surveillance data, a person associated with the triggering event, identify, using the at least one machine learning model, at least one characteristic of the person associated with the triggering event, generate an audio message comprising content based at least in part on the at least one characteristic of the person associated with the triggering event, and cause playback of the audio message in the area of the premises.

Abstract: A method implemented by a system for security comprising an unmanned aerial vehicle (UAV) and an analytics device configured to communicate with a remote monitoring system and the UAV is provided. Media data from the UAV is received at an analytics device, where the media data includes surveillance information corresponding to a premises under surveillance. Security attributes associated with the premises are detected based at least in part on a first level of machine learning (ML) analysis performed on the media data. The media data are transmitted by the analytics device to the remote monitoring system for a second level of ML analysis based at least in part on the security attribute, where the first level of ML analysis is less computationally expensive compared to the second level of ML analysis, and the second level of ML analysis is performed at the remote monitoring system on the media data.