Nvidia Corporation

Bhanu Nagendra Pisupati

Milind R Naphade

Parthasarathy Sriram

Arman Toorians

Sujit Biswas

Aaron Bartholomew

Ratnesh Kumar

Vinay Kolar

Joonhwa Shin

David Ung

Rohit Ramesh Vaswani

Rahul Maruti Bhagwat

The present disclosure provides various approaches for smart area monitoring suitable for parking garages or other areas. These approaches may include ROI-based occupancy detection to determine whether particular parking spots are occupied by leveraging image data from image sensors, such as cameras. These approaches may also include multi-sensor object tracking using multiple sensors that are distributed across an area that leverage both image data and spatial information regarding the area, to provide precise object tracking across the sensors. Further approaches relate to various architectures and configurations for smart area monitoring systems, as well as visualization and processing techniques. For example, as opposed to presenting video of an area captured by cameras, 3D renderings may be generated and played from metadata extracted from sensors around the area.

Scenario recreation through object detection and 3D visualization in a multi-sensor environment

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.

Workload-based dynamic throttling of video processing functions using machine learning

Smart area monitoring with artificial intelligence

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Cupertino, CA, United States of America

Bhanu Nagendra Pisupati