View source for Microsoft technology licensing, llc (20240129497). Reduced Video Stream Resource Usage simplified abstract

=Reduced Video Stream Resource Usage=


==Organization Name==

[[:Category:microsoft technology licensing, llc|microsoft technology licensing, llc]]


[[Category:microsoft technology licensing, llc]]

==Inventor(s)==

[[:Category:Yichen Hao of Redmond WA (US)|Yichen Hao of Redmond WA (US)]][[Category:Yichen Hao of Redmond WA (US)]]

[[:Category:Lihang Li of Bothell WA (US)|Lihang Li of Bothell WA (US)]][[Category:Lihang Li of Bothell WA (US)]]

[[:Category:Anthony C. Romano of Lebanon TN (US)|Anthony C. Romano of Lebanon TN (US)]][[Category:Anthony C. Romano of Lebanon TN (US)]]

[[:Category:Naiteek Sangani of Bothell WA (US)|Naiteek Sangani of Bothell WA (US)]][[Category:Naiteek Sangani of Bothell WA (US)]]

[[:Category:Ryan S. Menezes of Bellevue WA (US)|Ryan S. Menezes of Bellevue WA (US)]][[Category:Ryan S. Menezes of Bellevue WA (US)]]

==Reduced Video Stream Resource Usage - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240129497 titled 'Reduced Video Stream Resource Usage

==Simplified Explanation==

The patent application describes a resource-aware object detection system for encoded video streams that can identify frames containing a specific object without decoding the frames.

* This innovation allows for efficient object detection in video streams without the need to fully decode each frame.
* The system can identify frames with objects of interest, such as humans, based on encoded data, saving computational resources.
* By detecting objects in encoded video streams, this technology can improve real-time video analysis and surveillance applications.

== Potential Applications ==
The technology can be applied in various fields such as:
* Video surveillance systems
* Video analytics for security purposes
* Automated object tracking in videos

== Problems Solved ==
The technology addresses the following issues:
* Resource-intensive object detection in video streams
* Real-time identification of objects without full frame decoding
* Efficient utilization of computational resources for video analysis

== Benefits ==
The benefits of this technology include:
* Improved efficiency in object detection in video streams
* Reduced computational load for real-time video analysis
* Enhanced accuracy in identifying objects of interest

== Potential Commercial Applications ==
The technology can be commercially applied in:
* Security and surveillance industries
* Video analytics software development
* Smart city infrastructure for monitoring and analysis

== Possible Prior Art ==
One possible prior art could be the use of motion detection algorithms in video surveillance systems to identify objects of interest without fully decoding video frames.

== Unanswered Questions ==
=== How does the system handle different types of objects in the video stream? ===
The system likely uses object recognition algorithms to detect various types of objects based on predefined models and features.

=== What is the impact of the technology on video streaming services? ===
The technology could potentially improve video streaming services by enabling more efficient object detection and analysis without significant computational overhead.


==Original Abstract Submitted==

the description relates to resource aware object detection for encoded video streams that can identify frames of the video stream that include an object of interest, such as a human, without decoding the frames.



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