Abstract: Embodiments of the present disclosure relate to workload-based dynamic throttling of video processing functions. Systems and methods are disclosed that dynamically throttle video processing and/or streaming based on a workload. Live video is captured from one or more sources (e.g., cameras) and stored. The video is then provided to a video processing engine and a video streaming engine. The video processing engine may perform one or more operations such as object detection, object tracking, and object classification to produce characterization data (e.g., bounding boxes, object trajectories, alerts, object labels, object counts, boundary crossings, intersection highlighting, etc.). System resource usage and performance of the video processing and streaming are monitored to produce workload data (e.g., metrics). Based on the policies and the workload data, the video streaming and/or processing is dynamically reconfigured by adjusting parameters provided to the video streaming and processing engines.

Abstract: Embodiments of the present disclosure relate to workload-based dynamic throttling of video processing functions. Systems and methods are disclosed that dynamically throttle video processing and/or streaming based on a workload. Live video is captured from one or more sources (e.g., cameras) and stored. The video is then provided to a video processing engine and a video streaming engine. The video processing engine may perform one or more operations such as object detection, object tracking, and object classification to produce characterization data (e.g., bounding boxes, object trajectories, alerts, object labels, object counts, boundary crossings, intersection highlighting, etc.). System resource usage and performance of the video processing and streaming are monitored to produce workload data (e.g., metrics). Based on the policies and the workload data, the video streaming and/or processing is dynamically reconfigured by adjusting parameters provided to the video streaming and processing engines.

Abstract: A method of using a computer to analyze an extended finite state machine model of a system includes providing a graphical user interface that presents a table of rows, receiving data in at least one table row, and determining at least one path through the states and transitions of the extended finite state machine model using at least one of the rows in the table.