Microsoft technology licensing, llc (20240119089). CASCADED VIDEO ANALYTICS FOR EDGE COMPUTING simplified abstract
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 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 20240119089 titled 'CASCADED VIDEO ANALYTICS FOR EDGE COMPUTING
Simplified Explanation
The patent application relates to live video stream analytics on edge devices, prioritizing edge device processing over cloud processing to conserve resources.
- Resources are determined on the system to select a video analytics configuration that distributes work between edge and cloud devices.
- The allocation of processing can be dynamically modified based on changing conditions like network availability.
Potential Applications
This technology can be applied in various industries such as 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 network conditions.
Benefits
1. Reduced latency in video analytics processing. 2. Lower bandwidth usage by processing data closer to the source. 3. Improved scalability and flexibility in distributing processing tasks.
Potential Commercial Applications
Optimizing video surveillance systems, enhancing smart city infrastructure, and improving industrial automation processes through efficient video analytics on edge devices.
Possible Prior Art
Prior art in this field includes research on edge computing, video analytics, and resource allocation in distributed systems.
Unanswered Questions
How does this technology impact the overall performance of video analytics systems?
This technology can significantly improve the performance of video analytics systems by reducing latency and optimizing resource allocation.
What are the potential security implications of processing video data on edge devices?
Processing video data on edge devices may raise security concerns regarding data privacy, encryption, and secure communication protocols.
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.
