SCENE MARKING
The present disclosure overcomes the limitations of the prior art by providing approaches to marking points of interest in scenes. In one aspect, a Scene of interest is identified based on SceneData provided by a sensor-side technology stack that includes a group of one or more sensor devices. The SceneData is based on a plurality of different types of sensor data captured by the sensor group, and typically requires additional processing and/or analysis of the captured sensor data. A SceneMark marks the Scene of interest or possibly a point of interest within the Scene.
1 . A method implemented on a computer system for specifying and obtaining a higher level understanding of image data, the method comprising:
communicating a SceneMode to a sensor-side technology stack via an application programming interface (API), the sensor-side technology stack comprising a group of one or more sensor devices, wherein:
based on the SceneMode, a workflow that includes analysis of image data captured by the sensor devices is determined and executed by the sensor-side technology stack;
the workflow applies artificial intelligence and/or machine learning to the image data, and detects events based on the image data; and
the workflow generates SceneMarks triggered based on the events detected by the workflow, the SceneMarks comprising messages relating to the triggering events; and
receiving the generated SceneMarks from the sensor-side technology stack via the API.
2 . The computer-implemented method of claim 1 wherein the SceneMarks identify the SceneMode.
3 . The computer-implemented method of claim 1 wherein the SceneMode does not specify at least some of: the specific sensor devices used in the workflow, the specific sensor-level settings used in the workflow, the specific sensor data captured in the workflow, the specific processing and analysis used in the workflow, and the specific location of the processing and analysis used in the workflow.
4 . The computer-implemented method of claim 1 wherein the SceneMode does not specify at least some of the triggering events.
5 . The computer-implemented method of claim 1 wherein the artificial intelligence and/or machine learning is cloud-based, and at least some triggering events are detected by the cloud-based artificial intelligence and/or machine learning.
6 . The computer-implemented method of claim 1 wherein, based on the SceneMode, the triggering events include at least one of object recognition, recognition of humans, face recognition and emotion recognition.
7 . The computer-implemented method of claim 1 wherein multiple applications communicate SceneModes to the sensor-side technology stack via the API, and receive the resulting SceneMarks from the sensor-side technology stack via the API.
8 . The computer-implemented method of claim 7 wherein the SceneMarks identify the application communicating the SceneMode.
9 . The computer-implemented method of claim 8 further comprising:
storing the SceneMarks from different applications and making the SceneMarks available for subsequent searching and analysis, wherein at least some of the SceneMarks are generating by applying artificial intelligence and/or machine learning to previously stored SceneMarks.
10 . The computer-implemented method of claim 1 wherein the API, the SceneMode and a data structure for the SceneMarks are defined in one or more standard(s).
11 . The computer-implemented method of claim 10 wherein the standard(s) support SceneMark extensions.
12 . The computer-implemented method of claim 1 further comprising:
storing the SceneMarks and making the SceneMarks available for subsequent searching and analysis.
13 . The computer-implemented method of claim 1 wherein at least some of the SceneMarks are updated versions of previously generated SceneMarks.
14 . The computer-implemented method of claim 1 wherein at least some of the SceneMarks are generating by processing of previously generated SceneMarks.
15 . The computer-implemented method of claim 1 wherein the SceneMarks include provenance information that identify sources of the SceneMarks within the workflow.
16 . The computer-implemented method of claim 1 wherein the SceneMarks identify types of the triggering events.
17 . The computer-implemented method of claim 1 wherein the SceneMarks include alert levels based on the triggering events.
18 . The computer-implemented method of claim 1 wherein the SceneMarks include references to the image data on which the triggering events are based.
19 . The computer-implemented method of claim 1 wherein the SceneMarks identify relations to other SceneMarks.
20 . A non-transitory computer-readable storage medium storing executable computer program instructions for an application to specify and obtain a higher level understanding of image data, the instructions executable by a computer system and causing the computer system to perform a method comprising:
communicating a SceneMode to a sensor-side technology stack via an application programming interface (API), the sensor-side technology stack comprising a group of one or more sensor devices, wherein:
based on the SceneMode, a workflow that includes analysis of image data captured by the sensor devices is determined and executed by the sensor-side technology stack;
the workflow applies artificial intelligence and/or machine learning to the image data, and detects events based on the image data; and
the workflow generates SceneMarks triggered based on the events detected by the workflow, the SceneMarks comprising messages relating to the triggering events; and
receiving the generated SceneMarks from the sensor-side technology stack via the API.