18537291. CASCADED VIDEO ANALYTICS FOR EDGE COMPUTING simplified abstract (Microsoft Technology Licensing, LLC)
Contents
- 1 CASCADED VIDEO ANALYTICS FOR EDGE COMPUTING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 CASCADED VIDEO ANALYTICS FOR EDGE COMPUTING - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
CASCADED VIDEO ANALYTICS FOR EDGE COMPUTING
Organization Name
Microsoft Technology Licensing, LLC
Inventor(s)
Ganesh Ananthanarayanan of Seattle WA (US)
Yuanchao Shu of Bellevue WA (US)
Shadi Noghabi of Seattle WA (US)
Paramvir Bahl of Bellevue WA (US)
Alexander Crown of Bellevue WA (US)
CASCADED VIDEO ANALYTICS FOR EDGE COMPUTING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18537291 titled 'CASCADED VIDEO ANALYTICS FOR EDGE COMPUTING
Simplified Explanation
The document discusses live video stream analytics on edge devices, prioritizing edge device processing over cloud processing to conserve resources.
- Edge devices are prioritized over cloud devices for video analytics processing.
- Resources available to the system are determined to select a video analytics configuration.
- Work is distributed between edge devices and cloud devices in a cascading manner.
- The allocation of processing can be dynamically modified based on changing conditions, such as network availability.
Potential Applications
This technology can be applied in various industries such as security, surveillance, smart cities, and industrial automation for real-time video analytics on edge devices.
Problems Solved
1. Efficient resource utilization by prioritizing edge device processing. 2. Dynamic allocation of processing based on changing conditions like network availability.
Benefits
1. Improved performance and reduced latency in video analytics. 2. Cost-effective solution by utilizing edge devices efficiently.
Potential Commercial Applications
Optimizing video analytics in security systems for enhanced surveillance capabilities.
Possible Prior Art
There may be prior art related to edge computing and video analytics in the field of IoT devices and smart cameras.
Unanswered Questions
How does this technology impact the overall system performance in real-world scenarios?
The document does not provide specific data on the performance improvements achieved in real-world applications.
What are the potential limitations or challenges in implementing this technology on a large scale?
The document does not address the scalability challenges or potential limitations of deploying this technology across a wide range of edge devices and cloud infrastructure.
Original Abstract Submitted
This document relates to performing live video stream analytics on edge devices. One example determines resources available to the system, and a video analytics configuration is selected that distributes work between edge devices and cloud devices in a cascading manner, where edge device processing is prioritized over cloud processing in order to conserve resources. This example can dynamically modify the allocation of processing depending on changing conditions, such as network availability.
